Videotaped interviews as a medium to enhance cross-cultural programme evaluation
Bibliographic record
Abstract
Evaluation is a required component of interventions. Written data are the predominant source. However, video recording is used in many applications to evaluate a range of encounters and practices. We report assessment of the role of videotaped interviews in programme evaluation. Interviews using a consistent script of open-ended questions were recorded during evaluation of an international child-health promotion programme in Uganda by individuals with basic training and equipment. Participants were a convenience sample of programme team members (six school teachers, and six Ugandan and 12 Canadian health-care trainees) who had completed the annual written evaluation questionnaire. Evaluators reviewed each participant's videotaped interview and questionnaire, content coded the responses against a criterion-based check list, documented how many times factual information was contributed on each question and compared the data. Videos were also assessed for strong positive or negative emotion. Videotaped interviews provided more comprehensive responses than written questionnaires, and were more accurate where mis-comprehension of question meaning occurred. The video interview, unlike the written questionnaire, allowed rephrasing for clarification. The video interview medium enhanced programme evaluation by providing more facts, greater insight into the effects of the interventions and clearer direction for future activity. Hence, video-recorded feedback has great potential value in applied research for comprehensive programme evaluation.
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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 | 0.096 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".