Health systems research training enhances workplace research skills: A qualitative evaluation
Bibliographic record
Abstract
INTRODUCTION: In-service education is a widely used means of enhancing the skills of health service providers, for example, in undertaking research. However, the transfer of skills acquired during an education course to the workplace is seldom evaluated. The objectives of this study were to assess learner, teacher, and health service manager perceptions of the usefulness, in the work setting, of skills taught on a health systems research education course in South Africa and to assess the extent to which the course stimulated awareness and development of health systems research in the work setting. METHODS: The education course was evaluated using a qualitative approach. Respondents were selected for interview using purposive sampling. Interviews were conducted with 39 respondents, including all of the major stakeholders. The interviews lasted between 20 and 60 minutes and were conducted either face to face or over the telephone. Thematic analysis was applied to the data, and key themes were identified. RESULTS: The course demystified health systems research and stimulated interest in reading and applying research findings. The course also changed participants' attitudes to routine data collection and was reported to have facilitated the application of informal research or problem-solving methods to everyday work situations. However, inadequate support within the workplace was a significant obstacle to applying the skills learned. DISCUSSION: A 2-week intensive, experiential course in health systems research methods can provide a mechanism for introducing basic research skills to a wide range of learners. Qualitative evaluation is a useful approach for assessing the impacts of education courses.
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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 | 0.101 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".