Bridging research training and the public health system, results from a Training Program in Québec
Bibliographic record
Abstract
Background The Quebec Research Training Program on Public Health Interventions trained PhD and post-doctoral fellows in applied population health intervention research by using Public Health Organizations (PHO) as training laboratories and by creating University-PHO mentorship and supervisory partnerships. We present an evaluation of the impact of this program from 2003 to 2014. Methods Data were collected for trainees who had completed the Program, including administrative program data abstraction, online surveys of former trainees, mentors and unsuccessful applicants to the Program, in-depth interviews of trainees at the end of their training, self-administered questionnaires on program core competencies at program entry and exit, as well as telephone interviews with mentors and trainees by an independent evaluator. Descriptive and univariate analyses as well as thematic analysis were applied. Results Sixty-three graduate students from 31 disciplinary backgrounds were trained. Trainees developed a broad transdisciplinary research perspective and acquired competencies in building partnerships and knowledge translation skills. They published 244 peer-reviewed papers, 352 abstracts and 200 reports (including public health and policy documents) related to their research. Program graduates were more likely than unsuccessful applicants to now be conducting their research work in close proximity to public health organizations (58% vs 25%; p < 0.05) and to have obtained at least one funded research grant (56% vs 31%). A majority of mentors surveyed (71%) indicated that the University-PHO partnerships continued after the end of the internship. Conclusions The training program increased research capacity in population health intervention. Critical components include the strong links between research and practice, applied practice settings, transdisciplinary focus, and recurrent opportunities for interaction with peers, experts and practitioners from different disciplines. Key messages: This program has led to enhanced research skills in population health intervention, and abilities to navigate the complex interactions between research and practice in the field of public health Insuring continuity and sustainability of such training remains the main challenge at building a pertinent research capacity within the public health system
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".