Psychometric evaluation of a multi‐dimensional measure of satisfaction with behavioral interventions
Bibliographic record
Abstract
Treatment satisfaction is recognized as an essential aspect in the evaluation of an intervention's effectiveness, but there is no measure that provides for its comprehensive assessment with regard to behavioral interventions. Informed by a conceptualization generated from a literature review, we developed a measure that covers several domains of satisfaction with behavioral interventions. In this paper, we briefly review its conceptualization and describe the Multi-Dimensional Treatment Satisfaction Measure (MDTSM) subscales. Satisfaction refers to the appraisal of the treatment's process and outcome attributes. The MDTSM has 11 subscales assessing treatment process and outcome attributes: treatment components' suitability and utility, attitude toward treatment, desire for continued treatment use, therapist competence and interpersonal style, format and dose, perceived benefits of the health problem and everyday functioning, discomfort, and attribution of outcomes to treatment. The MDTSM was completed by persons (N = 213) in the intervention group in a large trial of a multi-component behavioral intervention for insomnia within 1 week following treatment completion. The MDTSM's subscales demonstrated internal consistency reliability (α: .65 - .93) and validity (correlated with self-reported adherence and perceived insomnia severity at post-test). The MDTSM subscales can be used to assess satisfaction with behavioral interventions and point to aspects of treatments that are viewed favorably or unfavorably.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".