Reliability of the Service Trip Audit Tool to assess the quality of short-term medical missions
Bibliographic record
Abstract
BACKGROUND: We aimed to assess the adherence of short-term medical missions (STMMs) operating in Latin America and the Caribbean (LAC) to key best practices using the Service Trip Audit Tool (STAT) and to calculate the inter-rater reliability of the data points. This tool was based on a previously published inventory of 18 STMM best practices. METHODS: Programme administrators and recent volunteers from 335 North American organizations offering STMMs in LAC were invited to complete the STAT anonymously online. Adherence to each of 18 best practices was reported as either 'yes', 'no' or 'not sure'. Fleiss' κ was used to assess inter-rater agreement of the responses. RESULTS: A total of 194 individuals from 102 organizations completed the STAT (response rate 30.4%; 102/335 organizations) between 12 July and 7 August 2017. Reported adherence was >80% for 9 of 18 best practices. For 37 non-governmental organizations (NGOs) with multiple raters, inter-rater agreement was moderate to substantial (κ>0.4) for 12 of 18 best practices. CONCLUSIONS: This is the first study to evaluate adherence to STMM best practices. Such an objective evaluation will be valuable to governments, volunteers and NGO donors who have an interest in identifying high-quality partners. Assessment and monitoring of STMMs through self-audit may be foundational steps towards quality improvement.
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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 | 0.070 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".