Formative, embedded evaluation to strengthen interdisciplinary team science: Results of a 4-year, mixed methods, multi-country case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evaluation of interdisciplinary, team science research initiatives is an evolving and challenging field. This descriptive, longitudinal, mixed methods case study examined how an embedded, formative evaluation approach contributed to team science in the interdisciplinary Research into Policy to Enhance Physical Activity (REPOPA) project, which focused on physical activity policymaking in six European countries with divergent policy systems and researcher–policymaker networks. We assessed internal project collaboration, communication, and networking in four annual data collection cycles with REPOPA team members. Data were collected using work package team and individual interviews, and quantitative collaboration and social network questionnaires. Interviews were content analyzed; social networks among team members and with external stakeholder were examined; collaboration scores were compared across 4 years using analysis of variance (ANOVA). Annual monitoring reports with action recommendations were prepared and discussed with consortium members. Results revealed consistently high response rates. Collaboration and communication scores, high at baseline, improved slightly, but ANOVA results were nonsignificant. Internal network changes tracked closely with implementation progress. External stakeholders were primarily governmental, with a marked shift from local/provincial level to national/international during the project. Diversity (disciplinary, organizational, and geopolitical) was a project asset influencing and also challenging collaboration, implementation, and knowledge translation strategies. In conclusion, formative evaluation using an embedded, participatory approach demonstrated utility, acceptability, and researcher engagement. A trusting relationship between evaluators and other project members built on joint identification of team science objectives for the evaluation at project outset, codeveloping guiding principles, and encouraging team reflexivity throughout the evaluation.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.284 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it