Template Analysis of a Longitudinal Interprofessional Survey: Making Sense of Free-Text Comments Collected Over Time
Bibliographic record
Abstract
Surveys are widely used in interprofessional education (IPE) research and these often collect free-text data. The potential contribution of free-text data to analysis and interpretation is often missed through separate reporting of qualitative and quantitative results, or free-text analyses being superficial or limited to subsets of data. There is little published guidance on how to maximize the use and integration of free-text comments with quantitative responses in large datasets collected over multiple years. Analysis of all qualitative comments, within the context of their related quantitative answers, enables exploration of changes in participants’ construction of meaning over time. This article describes how we used template analysis to analyze 3,626 free-text responses, collected as part of a five-year survey exploring the impact of an IPE program on health professionals’ attitudes to teamwork and early careers. We outline the main procedural steps undertaken by a team of researchers and we share our insights into the methodological challenges encountered. This article aims to inspire other researchers at the planning stage of research proposals, and assist them with practical ideas during data extraction, management, analysis, and reporting of large free-text datasets. We conclude that template analysis has methodologically sound, pragmatic utility in IPE longitudinal survey research
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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 | 0.134 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".