Journal of Addiction & Addictive Disorders Category: Clinical Type: Review Article

Improving Retention in Opioid Treatment

Daryl Mahon1*
1 Outcomes Matter, Ireland

*Corresponding Author(s):
Daryl Mahon
Outcomes Matter, Ireland
Tel:+353 0852770851,

Received Date: Jun 14, 2020
Accepted Date: Jul 03, 2020
Published Date: Jul 13, 2020


Problematic substance use significantly contributes to morbidity and mortality and affects individuals and communities systematically. Indeed, responses to such issues require a multidimensional, evidence informed approach. Medical, psychological and social responses have been identified as those with most efficacies within the literature. Illicit opiate use brings with it perhaps a more severe symptomatology when we consider the physiological phenomenon. Therefore, treatment needs to be multi-dimensional with both psychosocial and medical interventions. However, treatment attrition rates are very high with this cohort of people. Approaches to identify those at risk of dropout and poor outcomes have been established within the wider psychiatric and psychological domains. Client feedback entails soliciting data on outcome and process of care and utilising the information to adapt and tailor service deliver on a session to session (unit of care) basis. The present review paper seeks to build on previous research by extending these methods to the treatment of opioids in an Irish context.



Globally, we are witnessing a worsening opioid crisis with the United States of America exhibiting a lower overall life expectancy due to opioid use [1,2]. According to the World Drugs Report [3], opioids were the number one class of substances indicated in deaths by substance use in the 15-65 age category. Moreover, in 2016, a total of 42,249 died as a result of opioid use [4]. However, the most recent preliminary data [5], suggests mortality rates of approximately 72, 000 individuals have occurred within the previous 12-month period. Within Europe, 84% of deaths due to substance use have had opioids indicated as a contributing factor, up from 79% in 2017 [6].

The data from both these continents is indicative of major widespread health concerns. Thus, improved evidence-informed solutions are needed in order to reduce harm and support recovery in this cohort of people. However, treatment retention is needed in order to deliver such interventions. In an Irish context, psychosocial treatments for opiate use are associated with high dropout rates 53.5% for counselling and 59.9% psychosocial interventions. Thirty-day retention rates were reported as 76.5% and 61.6% respectively.

Worryingly, when we consider the often-longer term support needed for opiate treatment participants, retention at three months was 53.2% and 36.3%. respectively [7]. Opioids remain the most common primary drug among those entering treatment. 3560 individuals presented for treatment in 2018 [6]. From a personal, societal and public policy position, retaining and improving outcomes for those entering treatment will have differential benefits for society at large, however, current systems may need improvement to achieve these aims. Thus, the present paper explores the use of client feedback as a tool to improve treatment outcomes for this cohort of people.


The literature on addiction interventions provides us with robust research evidence of the efficacy of several approaches [8,9]. Moreover, evidence suggests [10], that psychosocial interventions have efficacy across the spectrum; including, brief and harm reduction interventions, motivational strategies and abstinence/rehabilitative based approaches. However, the gap between research and practice in naturalistic settings remains [11,12]. While psycho-social approaches work in various ways, medical interventions are also utilised for the physiological aspects of opioid addiction.

Mayet et al. [13], conducted a Cochrane Review and suggested that there was inadequate evidence during 2004 to support the efficacy of psychosocial interventions as a standalone treatment for opioid use. This is not surprising when we consider the wider evidence base exploring the effectiveness of medically assisted treatment as reported in several Cochrane reviews [14-19]. Yet, in her Irish data set research, Carnew [20], suggests that 83% of her study cohort attended for psychosocial interventions alone. Why this was the case has not been identified, however, stigmatisation around methadone use and how the protocols have historically been delivered could be one issue.

While methadone has shown to help retain people in treatment and reduce criminality two Irish studies provide for worrying research. Maycock et al. [21], suggest that the quality of life of many people in their study on opiate replacement treatment may not have improved. Indeed, in another Irish study found that clients voices were lost and feedback not listened to. ‘Service users described negative aspects centring on the patients lack of choices, humiliating experiences in consuming methadone in a public space, difficulties complying with punitive contracts and urine screening and engaging with uncaring service providers. This sentiment was echoed by Maycock et al. [21], study “a prominent feature of the treatment experience was a perception that, as clients or patients, they had no say in their treatment. By and large, participants felt controlled rather than in control with little evidence of them feeling able or entitled to share their experiences or to articulate any aspirations or needs related to their treatment”.

While the preceding quotes are more specific to the medical model mode of treatment, practitioners of psychosocial interventions also impact upon attrition rates through their relational interactions. Much has been discussed regarding the characteristics that can impact upon the treatment endeavour, especially related to the drop out phenomena.

However, other correlational factors can impede retention in treatment; for example, as many as 30% of clients with poly use drop out of treatment, alcohol-substance. Mode and modality can impact these statistics differentially, with detox drop out ranging from 21.5%-43% [22]; outpatient treatment programmes 23%-50% [23]; inpatient 17-53% [24]. In a cross-sectional study examining determinants of drop out ?im?eket et al. [25], found that “drop-out rate of the participants after the 2nd session was 42.5%. After the 5th session it reached to 78.2% and after the 10th session it reached to 93.9%. The highest drop-out rate was observed after the second session”. Broson et al. [26], conducted a systematic review that included 122 studies with 199,000 participants exploring drop out from treatment; they concluded that young age, mental health difficulties and the therapeutic alliance were key variables of drop out.

Research on the Therapeutic Alliance (TA) is well established, in fact, it is probably one of the most studied variables in therapy and one of the strongest predictors of therapy outcome [27-33].

In a key study of the therapeutic alliance, Baldwin et al. [34], elucidates the power of this construct by suggesting that 97% of the difference in client outcomes between therapists can be attributed to the alliance. Moreover, it was the clients rating of the alliance that was the important factor and notably, the clients’ contribution was not a variable for outcomes. Said another way, the difference in therapist outcomes is mainly due to their ability to build an alliance with different clients, who rate that alliance strongly, while clients’ contribution has little in the way of impact on alliance contribution.

Conversely, clients of therapists who cultivate weaker alliances tend to drop out at higher rates and experience poorer outcomes [35,36]. This research is consistent across general or substance use therapy. Thus, monitoring of the therapeutic alliance would seem necessary. Notably, the interventionists ability to establish a bond, collaborative agreement on the task, goals and methods to be used in treatment, is essential, as outlined by conceptualisation of the therapeutic alliance. Heinonen et al. [37], contend that “the ability to professionally relate to clients with empathy, warmth, positive regard, clear and positive communication and the capacity to take critical feedback predicted better outcomes across all levels of therapist expertise”


Routine Outcome Monitoring (ROM), also referred to as feedback informed or systemic feedback, is gathering support over the last decade in both academic and practice settings. Vast literature on this approach postulates that intentionally and formally soliciting live feedback from clients on a session by session basis can improve therapy outcomes, reduce dropout rates and identify those at risk for deterioration [38-40].

This method of assessing clients in an empirical and standardised manner may be needed as research suggests that practitioners do not adequately predict the deterioration of clients, those at risk of dropping out or at risk of null outcomes when assessing these issues informally [41].

Although routine outcome monitoring and feedback informed treatment are widely supported in the research as pan-theoretical practice-based evidence approaches, across broader psychiatric and psychological environments, specificity is very often the criteria sought out by policy makers and commissioning bodies. Therefore, it is incumbent on the review to highlight studies of client feedback approaches within this specific arena.

However, there is a relative dearth of research specifically addressing this question in addiction settings in general and opioid use specifically. McCaul and Svikis [42], showed that providing clinicians with feedback on client attendance subsequently improved retention rates. Schuman et al. [43], conducted research into the use of feedback with soldiers attending group therapy (n=263) for substance use. Compared to the control group which consisted of group therapy treatment as usual, the experimental group showed moderate effect size gains and attended more sessions. Crits-Christoph et a.l [44], showed that for off-track patients, “feedback compared with no feedback led to significant linear reductions in alcohol use throughout treatment and also in OQ-45 total scores and drug use from the point of the second feedback instrument to Session 12”, Brorson et al. [45], failed to predict those at risk of dropout within a small sample utilising the OQ -Analyst, however, notably, measures of alliance were not used in conjunction with this outcome measurement tool.

Likewise, Crits-Christoph et al. [46], found no effects of feedback, however, it was identified that providing aggregated scores at the group level as opposed to individual clients left practitioners unmotivated to use this feedback. This would seem to reflect the issues in the Delargy et al. study where informal feedback was not listened to or acted on. Raes et al. found that the group that received assessment and feedback were significantly more likely to remain in treatment at and beyond 8 and 12 weeks. This is an important finding considering the attrition rates that we currently exhibit in services. While these studies show some good and promising results, they equally demonstrate limited methodologies, such as non-randomisations and lack of appropriate outcome measures.

However, a strong aspect of these studies is the diversity of ethnicity, age, substance use and socioeconomic status, in addition to the environments the research were carried out in (drug courts, community settings, inpatient).Thus, the heterogeneous composition lends credence to the findings and it is not overly ambitious to postulate that the use of client feedback in substance use will likely mirror that of the wider behavioural health care arena, where it has been given evidence based recognition by American Psychological Association (APA).

In a study exploring the acceptability of feedback approaches in substance use treatment, both clients and practitioners highlighted its value and usefulness for treatment planning [47]. Tyron et al. [48], meta-analysis of goal consensus and collaboration posit that “results suggest patient–therapist goal consensus and collaboration enhance psychotherapy outcome” At the same time, two meta-analyses Swift et al. [49] and Lindhiem et al. [50], demonstrated that preference accommodation within treatment produces better client outcomes. Hence, the call by Carlier and Van Eeden [51], who suggest that training should be provided to clinicians in administration, interpretation and using feedback to discuss treatment goals and to ignite a culture of feedback within service provision. Baldwin et al. [34], expand on this point from their alliance study by concluding that “clinical implications include therapists monitoring their contribution to the alliance, clinics providing feedback to therapists about their alliances and therapists receiving training to develop and maintain strong alliances”

Several psychometrically sound instruments with normative data are available in order to work within a feedback informed approach [52-54]. Generally, feedback is solicited based on measures assessing the quality of the therapeutic alliance and outcome questionnaires based on specific or global levels of distress. Hatfield and Ogles conducted a national survey of psychologists and found that uptake of such instruments was limited due to perceived barriers such as; time and money and practicalities of their in-session brevity. This may be true for longer more tiresome instruments such as the Outcome Questionnaire-45 or the Symptom Checklist-90. However, shorter tools such as the Outcome Rating Scale (ORS) or the Clinical Outcomes in Routine Evaluation (CORE10) have utility without losing much validity or reliability; more, their in-session brevity and utility are high. However, pre-defined outcome measures, or often politicalised, especially in the substance use domain where commissioning bodies mandate their use. In a scoping review of the outcome measurement literature in the substance use field Alves, Sales and Ashworth [55], found 42 measure covering 54 domains. While the measures generally covered the important topics, “we found that several topics of relevance for patients were not covered by any of the measures included in our study”.

Thus, the need for measures that can capture subjective and important issues for clients are needed. However, it is also integral that such instruments are reliable, valid and have clinical utility and brevity. The Outcome Rating Scale (ORS) is used in conjunction with the Session Rating Scale (SRS) as the main protocols in Feedback Informed Treatment (FIT). Both tools are ultra-brief 4 item measures, capturing the process and outcome of care. Importantly, both measures meet the criteria above, as they have strong psychometric properties, and are brief subjective measures. In relation to the ORS, the 4 items (individual, interpersonal, social and overall) are a reflection of general-well being as identified by the client’s subjective experience as opposed to pre-determined domains that providers and commissioning bodies put forward.

Although measurement of outcomes is utilised in some services, these are generally used to evidence outcomes for commissioning bodies as opposed to dialogue tools for therapeutic conversations and are used at pre-post destinations. At the same time, excellent research is carried out in naturalistic settings assessing the needs of clients within services to influence policy and practice, yet some limitations are evident here to. Current processes often consist of longitudinal, retrospective programme/interventions or process evaluations presented thematically that identify service user’s needs and experiences [21,56-59] and are thus extrapolated to future services users or policy positions. Although providing important and rich research in different contexts, such methodologies do not capture live data which can be used to adapt the treatment approach in real time, based on client preferences and needs as they relate to both process and outcomes of care [60-74].


The treatment of opioid dependency represents a particular challenge for treatment practitioners, providers and policy makers. Although we have many different interventions across the broader psychosocial and medical domains, keeping this cohort of people engaged in the treatment process long enough to benefit from such interventions is problematic. Much research has been conducted on the client factors that correlate with early termination from the treatment endeavour. However, less has been explicitly carried out on treatment provider factors that impact this issue. Yet, it would seem that the therapeutic alliance plays a key role in mediating client satisfaction and ultimately positive outcomes.

The research literature presents a compelling case for involving those in our care to have a voice in their treatment approach. Establishing client preferences through the use of outcome measures is robustly supported within the extent literature. Within the Irish context it is clear that listening to the voices of clients is necessary, not just because it serves a clinical purpose by improving outcomes. But, also, at a human and social justice level, clients should be able to feel empowered and voice their concerns, preferences, wishes and needs. Feedback Informed Treatment (FIT) is one method well positioned to help those working with clients to articulate their needs in a formalised manner and use the resulting information to address these needs in real time.


  1. McGinnis JM, Foege WH (1999) Mortality and morbidity attributable to use of addictive substances in the United States. Proc Assoc Am Physicians 111: 109-118.
  2. Hunter SB Ober AJ, Paddock SM, Hunt PE, Levan D (2014) Continuous Quality Improvement (CQI) in addiction treatment settings: Design and intervention protocol of a group randomized pilot study. Addict Sci Clin Pract 9: 4.
  3. World Drugs Report (2018) Analysis of drug markets opiates, cocaine, cannabis, synthetic drugs. United Nations publication.
  4. Centres for Disease Control and Prevention (2018) Annual surveillance report of drug-related risks and outcomes. Centers for Disease Control and Prevention, USA.
  5. Ahmad FB, Rossen LM, Spencer MR, Warner M, Sutton P (2018) Provisional drug overdose death counts. National Center for Health Statistics.
  6. European Monitoring Center for Drugs and Alcohol (2018) Preventing Overdose Deaths in Europe, Perspectives of Drugs. EMCDDA, Lisbon, Portugal.
  7. Carew AM, Comskey C (2018) Rising incidence of ageing opioid users within the EU wide treatment demand indicator; The Irish opioid epidemic from 1996 to2014. Drug Alcohol Depend 192: 329-337.
  8. Bates G, Jones L, Maden M, Corchrane M, Pendlebury M, et al. (2017) The effectiveness of interventions related to the use of illicit drugs: Prevention, harm reduction, treatment and recovery. A ‘review of reviews’. HRB Drug and Alcohol Evidence Review. Dublin: Health Research Board.
  9. McGovern R, Addison MT, Newham JJ, Hickman M, Kaner EFS (2017) Effectiveness of psychosocial interventions for reducing parental substance misuse. Cochrane Database Syst Rev 10: 012823.
  10. Chadda R, Chatterjee B (2018) Need for psychosocial interventions: From resistance to therapeutic alliance. Indian J Psychiatry 60: 440-443.
  11. Horigian VE, Espinal PS, Alonso E, Verdeja R, Marín-Navarrete R, et al. (2014) Readiness and barriers to adopt evidence-based practices for substance abuse treatment in Mexico. Alcohol and Alcoholism 49: 44.
  12. Ramsey A (2015) Integration of technology-based behavioral health interventions in substance abuse and addiction services. International journal of mental health and addiction 13: 470-480.
  13. Mayet S, Farrell M, Ferri M, Amato L, Davoli M (2005) Psychosocial treatment for opiate abuse and dependence. Cochrane Database Syst Rev 1: 004330.
  14. Clark N, Lintzeris N, Gijsbers A, Whelan G, Dunlop A, et al. (2003) LAAM maintenance vs methadone maintenance for heroin dependence. Cochrane Database Syst Rev 2: 002210.
  15. Ferri 2003 Ferri M, Davoli M, Perucci CA (2003) Heroin maintenance for chronic heroin dependents. Cochrane Database of Systematic Reviews 4.
  16. Faggiano F, Vigna-Taglianti F, Versino E, Lemma P (2003) Methadone maintenance at different dosages for opoid dependence. Cochrane Database Syst Rev 3: 002208.
  17. Mattick RP, Kimber J, Breen C, Davoli M (2003) Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev 2: 002207.
  18. Mattick RP, Breen C, Kimber J, Davoli M (2003) Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev 2: 002209.
  19. Minozzi S, Amato L, Vecchi S, Davoli M, Kirchmayer U, et al. (2006) Oral naltrexone maintenance treatment for opioid dependence. Cochrane Database Syst Rev 4: 001333.
  20. Carew AM (2019) Routine health surveillance data: A 21-year quantitative cross-sectional study of first-time opioid addiction treatment among older adults, Trinity College Dublin, Ireland.
  21. Mayock P, Butler S, Hoey D (2018) “Just maintaining the status quo”? The experiences of long-term participants in methadone maintenance treatment. Dublin: Dun Laoghaire Rathdown Drug and Alcohol Task Force.
  22. Gilchrist G, Langohr K, Fonseca F (2012) Factors associated with discharge against medical advice from an inpatient alcohol and drug detoxification unit in Barcelona during 1993 and 2006. Heroin Addiction and Related Clinical Problems 14: 35-43.
  23. McHugh RK, Murray HW, Hearon BA (2013) Predictors of dropout from psychosocial treatment in opioid-dependent outpatients. Am J Addict 22: 18-22.
  24. Samuel DB, LaPaglia DM, Maccarelli LM (2011) Personality disorders and retention in a therapeutic community for substance dependence. Am J Addict 20: 555-562.
  25. ?im?ek M, Dinç M, Ögel K (2018) Determinants of the addiction treatment drop-out rates in an addiction counseling centre: A cross-sectional study. Psychiatry and Clinical Psychopharmacology 29: 446-454.
  26. Brorson HH, Arnevik EA, Rand-Hendriksen K, Duckert F (2013) Drop-out from addiction treatment: A systematic review of risk factors. Clin Psychol Rev 33: 1010-1024.
  27. Anker MG, Duncan BL, Sparks JA (2009) Using client feedback to improve couple therapy outcomes: A randomized clinical trial in a naturalistic setting. J Consult Clin Psychol 77: 693-704.
  28. Anker MG, Duncan BL, Owen J, Sparks JA (2010) The alliance in couple therapy: Partner influence, early change and alliance patterns in a naturalistic sample. J Consult Clin Psychol 78: 635-645.
  29. Horvath AO, Symonds BD (1991) Relation between working alliance and outcome in psychotherapy: A meta-anaysis. Journal of Counseling Psychology 38: 139-149.
  30. Martin D, Garske J, Davis M (2000) Relation of the therapeutic alliance with other outcome and other variables: A meta-analytic review. Journal of Consulting and Clinical Psychology 68: 438-450.
  31. Norcross JC (2011) Psychotherapy relationships that work: Evidence-based responsiveness (2nd). Oxford University Press, New York, USA.
  32. Orlinsky DE, Rønnestad MH, Willutzki U (2004) Fifty years of psychotherapy process-outcome research: Continuity and change. Wiley, New York, USA.
  33. Safran JD, Muran JC, Proskurov B (2010) Alliance, negotiation and rupture resolution.
  34. Baldwin SA, Wampold BE, Imel ZE (2007) Untangling the alliance-outcome correlation: exploring the relative importance of therapist and patient variability in the alliance. J Consult Clin Psychol 75: 842-852.
  35. American Psychological Association (2010) The heart and soul of change: Delivering what works in therapy. American Psychological Association, Washington, DC, USA.
  36. Lambert MJ (2010) Prevention of treatment failure: The use of measuring, monitoring, and feedback in clinical practice. American Psychological Association, Washington, DC, USA.
  37. Heinonen E, Nissen-Lie H (2019) The professional and personal characteristics of effective psychotherapists: A systematic review. Psychotherapy Research 30: 417-432.
  38. Brattland H, Koksvik JM, Burkeland O, Gråwe RW, Klöckner C, et al. (2018) The effects of Routine Outcome Monitoring (ROM) on therapy outcomes in the course of an implementation process: A randomized clinical trial. J Couns Psychol 65: 641-652.
  39. Dyason KM, Shanley DC, O'Donovan A, Low-Choy S (2019) Does feedback improve psychotherapy outcomes compared to treatment-as-usual for adults and youth? Psychother Res 30: 310-324.
  40. Lambert MJ, Whipple JL, Kleinstäuber M (2018) Collecting and delivering progress feedback: A meta-analysis of routine outcome monitoring. Psychotherapy (Chic) 55: 520-537.
  41. Hall CL, Moldavsky M, Taylor J, Sayal K, Marriott M, et al. (2014) Implementation of routine outcome measurement in child and adolescent mental health services in the United Kingdom: A critical perspective. Eur Child Adolesc Psychiatry 23: 239-242.
  42. McCaul ME, Svikis DS (1991) Improving client compliance in outpatient treatment: Counselor-targeted interventions. NIDA Res Monogr 106: 204-215.
  43. Schuman DL, Slone NC, Reese RJ, Duncan B (2014) Efficacy of client feedback in group psychotherapy with soldiers referred for substance abuse treatment. Psychother Res 25: 396-407.
  44. Crits-Christoph P, Ring-Kurtz S, Hamilton JL, Lambert MJ, Gallop R, et al. (2012) A preliminary study of the effects of individual patient-level feedback in outpatient substance abuse treatment programs. J Subst Abuse Treat 42: 301-309.
  45. Brorson HH, Arnevik EA, Rand K (2019) Predicting dropout from inpatient substance use disorder treatment: A prospective validation study of the OQ-Analyst. Subst Abuse: 13: 1178221819866181.
  46. Crits-Christoph P, Ring-Kurtz S, McClure B, Temes C, Kulaga A (2010) A randomized controlled study of a web-based performance improvement system for substance abuse treatment providers. J Subst Abuse Treat 38: 251-262.
  47. Johnston KL, Lawrence SM, Dodds NE, Yu L, Daley DC (2016) Evaluating PROMIS® instruments and methods for patient-centered outcomes research: Patient and provider voices in a substance use treatment setting. Qual Life Res 25: 615-624.
  48. Tryon GS, Birch SE, Verkuilen J (2018) Meta-analyses of the relation of goal consensus and collaboration to psychotherapy outcome. Psychotherapy 55: 372-383.
  49. Swift JK, Callahan JL, Cooper M, Parkin SR (2018) The impact of accommodating client preference in psychotherapy: A meta-analysis. J Clin Psychol 74: 1924-1937.
  50. Lindhiem O, Bennett CB, Trentacosta CJ, McLear C (2014) Client preferences affect treatment satisfaction, completion, and clinical outcome: a meta-analysis. Clin Psychol Rev 34: 506?
  51. Carlier IVE, van Eeden WA (2017) Routine outcome monitoring in mental health care and particularly in addiction treatment: Evidence-based clinical and research recommendations. Journal of Addiction Research and Therapy 8: 332.
  52. Duncan BL, Miller SD, Sparks D (2003) The session rating scale: Preliminary psychometric properties of a “working alliance” inventory. Journal of Brief Therapy 3: 3-12.
  53. Miller, Bargmann (2012) The outcome and session rating scales: A brief overview. Integrating Science and Practice.
  54. Maruish ME (2004) The use of psychological testing for treatment planning and outcomes assessment. Routledge, London, United Kingdom.
  55. Alves P, Sales C, Ashworth M (2017) Does outcome measurement of treatment for substance use disorder reflect the personal concerns of patients? A scoping review of measures recommended in Europe. Drug Alcohol Depend 179: 299?
  56. Babineau K (2016) Pathways through treatment: a mixed-methods longitudinal outcomes study of Coolmine therapeutic community.
  57. Ivers JH, Larkan F, Barry J (2018) A longitudinal qualitative analysis of the lived experience of the recovery process in opioid-dependent patients post-detoxification. J Psychoactive Drugs 3: 231-239.
  58. Ivers, Jo-Hanna H, Barry, Joseph (2018) An evaluation of Tabor Group. Cork: Tabor Group.
  59. Barkham M, Bewick B, Mullin T, Gilbody S, Connell J, et al. (2012) The CORE-10: A short measure of psychological distress for routine use in the psychological therapies. Counselling and Psychotherapy Research 13: 3-13.
  60. Berking M, Orth U, Lutz W (2006) How effective is systematic feedback of treatment progress to the therapist? An empirical study in a cognitive-behavioural-oriented inpatient setting [Wie effektiv sind systematische Rückmeldungen des Therapieverlaufs an den Therapeuten?...]. Forschung und Praxis 35.
  61. Connell J, Grant S, Mullin T (2006) Client initiated termination of therapy at NHS primary care counselling services. Couns Psychother Res 6: 60-67.
  62. Cox G, Cullen B (2002) Aislinn adolescent addiction treatment centre evaluation report. HRB National Drugs Library.
  63. Deane FP, Wootton DJ, Hsu CI, Kelly PJ (2012) Predicting dropout in the first 3 months of 12-step residential drug and alcohol treatment in an Australian sample. J Stud Alcohol Drug 73: 216-225.
  64. Elliot J (2005) Using Narrative in Social Research. Qualitative and Quantitative Approaches. Sage Publications, London, UK.
  65. Gilchrist G, Langohr K, Fonseca F (2012) Factors associated with discharge against medical advice from an inpatient alcohol and drug detoxification unit in Barcelona during 1993-2006. Heroin Addiction and Related Clinical Problems 14: 35-44.
  66. Giraudon F, Mathis L, Montanari T, Seyler J, Matias D, et al. (2019) Drug related deaths and mortality in Europe. EMCDDA, Lisbon, Portugal.
  67. Herschell AD, Kolko DJ, Baumann BL, Davis AC (2010) The role of therapist training in the implementation of psychosocial treatments: a review and critique with recommendations. Clin Psychol Rev 30: 448-466.
  68. Imel ZE, Wampold BE, Miller SD, Fleming RR (2008) Distinctions without a difference: Direct comparisons of psychotherapies for alcohol use disorders. Psychology of Addictive Behaviors 22: 533-543.
  69. Kaiser T, Schmutzhart L, Laireiter AR (2018) Attitudes of Austrian psychotherapists towards process and outcome monitoring. Administration and Policy in Mental Health 45: 765-779.
  70. Lambert MJ (2017) Maximizing psychotherapy outcome beyond evidence-based medicine. Psychother Psychosom 86: 80-89.
  71. McKellar J, Kelly J, Harris A (2013) Pre treatment and during treatment risk factors for dropout among patients with substance use disorders. Addict Behav 31: 450-460.
  72. Miller SD, Duncan BL, Brown J (2006) The outcome rating scale: A preliminary study of the reliability, validity, and feasibility of a brief visual analogue measure. Journal of Brief Therapy 2.
  73. Wampold BE (2015) How important are the common factors in psychotherapy? An update. World Psychiatry 14: 270-277.
  74. Wampold BE (2013) The great psychotherapy debate: Models, methods and findings. Routledge, London, United Kingdom.

Citation: Mahon D (2020) Improving Retention in Opioid Treatment. J Addict Addictv Disord 7: 47.

Copyright: © 2020  Daryl Mahon, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Herald Scholarly Open Access is a leading, internationally publishing house in the fields of Sciences. Our mission is to provide an access to knowledge globally.

© 2023, Copyrights Herald Scholarly Open Access. All Rights Reserved!