A critical analysis of user satisfaction surveys in addiction services: opioid maintenance treatment as a representative case study
Bibliographic record
Abstract
Joan Trujols,1,2 Ioseba Iraurgi,3 Eugenia Oviedo-Joekes,4,5 Joan Guàrdia-Olmos61Unitat de Conductes Addictives, Servei de Psiquiatria, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB Sant Pau), Barcelona, 2Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, 3DeustoPsych – Unidad de Investigación, Desarrollo e Innovación en Psicología y Salud, Universidad de Deusto, Bilbao, Spain; 4School of Population and Public Health, University of British Columbia, 5Centre for Health Evaluation and Outcome Sciences, Providence Health Care Research Institute, Vancouver, Canada; 6Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Institut de Recerca en Cervell, Cognició i Conducta (IR3C), Universitat de Barcelona, Barcelona, SpainBackground: Satisfaction with services represents a key component of the user's perspective, and user satisfaction surveys are the most commonly used approach to evaluate the aforementioned perspective. The aim of this discursive paper is to provide a critical overview of user satisfaction surveys in addiction treatment and harm reduction services, with a particular focus on opioid maintenance treatment as a representative case.Methods: We carried out a selective critical review and analysis of the literature on user satisfaction surveys in addiction treatment and harm reduction services.Results: Most studies that have reported results of satisfaction surveys have found that the great majority of users (virtually all, in many cases) are highly satisfied with the services received. However, when these results are compared to the findings of studies that use different methodologies to explore the patient's perspective, the results are not as consistent as might be expected. It is not uncommon to find that “highly satisfied” patients report significant problems when mixed-methods studies are conducted. To understand this apparent contradiction, we explored two distinct (though not mutually exclusive) lines of reasoning, one of which concerns conceptual aspects and the other, methodological questions.Conclusion: User satisfaction surveys, as currently designed and carried out in addiction treatment and harm reduction services, do not significantly help to improve service quality. Therefore, most of the enthusiasm and naiveté with which satisfaction surveys are currently performed and interpreted – and rarely acted on in the case of nonoptimal results – should be avoided. A truly participatory approach to program evaluation is urgently needed to reshape and transform patient satisfaction surveys.Keywords: patient satisfaction, substance abuse treatment services, harm reduction services, patient-centered evaluation, service user perspective, user involvement
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".