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
Bibliographic record
Abstract
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 Pro blem-S o lving Therapy F idelity (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 AC 1 . 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 AC 1 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".