A critical analysis of user satisfaction surveys in addiction services: opioid maintenance treatment as a representative case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it