The contribution of participant, treatment, and outcome factors to treatment satisfaction
Bibliographic record
Abstract
Treatment satisfaction, which refers to the positive appraisal of process and outcome attributes of a treatment, is a prominent indicator of quality care. Although it is known that participant, treatment, and outcome factors influence treatment satisfaction, it remains unclear which factors contribute to satisfaction with each process and outcome attribute. In this study, we examined the extent to which participant (age, gender, education, race, employment), treatment (type of therapy, method of assignment to therapy), and outcome (self-reported improvement in outcome) factors contribute to satisfaction with the process and outcome attributes of therapies for insomnia. This study consists of a secondary analysis of data obtained from a partially randomized preference trial in which persons with chronic insomnia (N = 517) were assigned to treatment randomly or by preference. Four types of behavioral therapies were included: sleep hygiene, stimulus control therapy, sleep restriction therapy, and multi-component therapy. Self-reported improvement in insomnia and satisfaction were assessed with validated measures at post-test. Multiple regression analysis was used to examine which factors influenced satisfaction with each treatment attribute. The findings showed that treatment and outcome, more so than participant, factors influenced satisfaction with the process and outcome attributes of the behavioral therapies for insomnia. Future research on satisfaction should explore the contribution of treatment (type and preference-matching) and outcome factors on satisfaction to build a better understanding of treatment attributes viewed favorably. Such understanding has the potential to inform modifying or tailoring treatments to improve their acceptance to participants and optimize their effectiveness.
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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 | 0.001 | 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".