“Setting people up for success and then failure” – health care and service providers’ experiences of using prize-based contingency management
Bibliographic record
Abstract
BACKGROUND: Over the last 50 years, there has been a growing interest in and use of contingency management (CM) for people who use substances. Yet, despite showing some level of efficacy (albeit only short-term) and being praised by researchers as beneficial and cost-saving, it continues to be underutilized by health care and service providers. Why that is remains unclear. METHODS: Recognizing a gap, we conducted a targeted analysis of a larger set of qualitative interviews conducted on the experience of health care and service providers with incentives (including prize-based CM) (n = 25). RESULTS: Four themes were identified during the analysis: 1) The specificities of prize-based CM, 2) The role of providers in administering prize-based CM, 3) The positive and negative impact on the relationship, and 4) The ethical concerns arising from prize-based CM. Overall, our findings are consistent with existing literature and suggest that providers are wary of using prize-based CM because they tend to value effort over success, support over reward, honesty over deceit, and certainty over probability and variability. CONCLUSION: Our analysis offers additional insights into the experiences of providers who use prize-based CM and possibly some indications as to why they may not wish to work with this type of incentive. The question raised here is not whether there is enough evidence on the effectiveness of prize-based CM, but rather if this type of incentive is appropriate and ethical when caring for people who use substances.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".