Population health intervention research training: the value of public health internships and mentorship
Bibliographic record
Abstract
Abstract Background Better alignment between academia and public health practice and policies are critical to improve public health actions. Training of future researchers to address complex issues and to conduct transdisciplinary and collaborative research will help improve this alignment. In this paper, we describe the role of internship placements and mentorship for trainees’ skills development in population health intervention research and the benefits of embedding research trainees within public health organizations. Methods This qualitative descriptive study assessed the perceptions of the role and benefits of internships and mentorship for population health intervention research training among former doctoral and postdoctoral students, public health mentors, and senior public health managers who participated in the 4P Program, a research training program which bridges academic training and the public health system in Quebec, Canada. Two types of interviews were conducted: telephone semi-structured interviews by an external evaluator and face-to-face trainee “exit” interviews by the Program co-director. Semi-annual evaluation reports from each trainee were also reviewed. Qualitative data were subjected to a thematic analysis. Results Internships provided trainees with a working knowledge of the public health system and the context in which decisions and public health interventions are implemented. It was an opportunity for trainees to interact with knowledge-user partners and assess the gap between research and practice. Effective mentorship was key to help trainees interpret the public health reality and develop population health intervention research skills. Trainees learned to ask the “how” questions that are critical for in-depth understanding of complex interventions and the conditions under which they can be best implemented. Conditions of success of internships and mentorship for population health intervention research included the alignment of the interests between the trainee, the mentor and the public health organization, quality mentoring, and the acquisition of specific population health intervention skills, especially collaborative research skills. Conclusions The findings suggest that public health internships and mentorship facilitate trainee engagement in applied public health 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 | 0.054 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".