Training Program in Reproduction, Early Development, and the Impact on Health (REDIH): Evaluation of Year 1
Bibliographic record
Abstract
Objectives : The purpose of this research was to use the W(e)Learn conceptual framework to design, deliver and evaluate the Reproduction, Early Development, and the Impact on Health (REDIH) training program for graduate students and post-doctoral fellows. Methods: The REDIH program provides stipends and other support, and runs semi-annual two-day face-to-face training sessions for trainees with their mentors. During the sessions, seminars and workshops are provided, and laboratory visits are arranged for trainees. A mixed methods approach (surveys and focus groups) was used to evaluate the content, delivery, structure and service of the first year of the REDIH training program. Results : Trainees recognized and appreciated three main improvements implemented into the second REDIH training session as a result of their feedback: (a) objectives and expectations were made clearer, (b) laboratory visits and more hands-on learning had been implemented, and (c) segregation between trainees and mentors had been greatly reduced. Trainees also had several recommendations for further improvements. Conclusions: Trainees were overwhelmingly appreciative of and grateful for the opportunity to be involved in the REDIH project. Trainees felt their voices had been heard during the first training session and steps were taken to address their expressed concerns and needs in the second session. This study also demonstrated that evaluation is critical for program design, improvement and long-term success. Perceptions of quality were strongly linked to a fit between participants’ experiences, needs, wants, and perceived competencies; a formal evaluation process; and project administrators and the curriculum committee respecting and responding to the participants’ feedback via the evaluators.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".