Decision: Development and preliminary inter-rater reliability of the new PROOF tool to measure fidelity of problem-solving therapy for depression delivered by non-specialists in a low-resource African setting — R0/PR7
Bibliographic record
Abstract
Problem-solving therapy (PST) is a brief psychological intervention often implemented for depression. Currently, there are no tools with well-evidenced reliability to measure PST fidelity. This pilot study aimed to measure the inter-rater reliability and agreement of the Problem-Solving Therapy Fidelity (PROOF) scale, comprising binary 14-item adherence and an 8-item competence subscales. Transcripts were from the TENDAI trial, a Zimbabwe-based PST intervention for depression and medication adherence. Seven transcripts were each rated by seven specialists, and two transcripts were each rated by two non-specialists. Inter-rater agreement was assessed using percent agreement and inter-rater reliability was assessed using Gwet’s AC1. The PROOF subscales demonstrated promising inter-rater agreement among specialists (adherence = 90.4%, competence = 82.5%) and non-specialists (adherence = 92.9%, competence = 68.8%). Inter-rater reliability analyses yielded a Gwet’s AC1 of 0.411–0.778 and 0.619–0.959 for adherence and competence among specialists, and 0.529–1.00 for adherence in non-specialists. The PROOF scale has the potential to fill the gap of fidelity tools for PST delivery.
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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 | 0.119 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".