Healthy public policy competences for public health: interactive and practice-oriented curriculum
Bibliographic record
Abstract
Abstract Background Developing and implementing healthy public policy (HPP) is one of the practical competences expected of public health professionals in Europe and beyond (ASPHER 2018, Public Health Agency of Canada 2015, US Public Health Foundation 2014). Yet, organizational practices in building public health capacity to promote HPP are seldom documented. In order to improve its HPP interventions, the Montreal Public Health Unit has been leading and evaluating a HPP multidisciplinary community of practice since 2014. In response to participants’ requests, we recently formalized a HPP curriculum. Objectives Over a period of 12 months in 2018-2019, our objectives were to: (1) develop and pre-test a competency-based professional development curriculum in HPP for public health staff and interns (2) provide a repository of relevant references (3) identify dynamic pedagogical strategies applicable to a community of practice. Results As community of practice members and facilitators, we developed a HPP curriculum with the support of pedagogical and HPP experts. It was pre-tested and iteratively adjusted with members of the community of practice. We drew content from various disciplines including political sciences, public health, communication studies and public relations. We identified relevant: (1) competences; (2) core concepts; (3) practical skills; (4) key references; (5) practical case studies, (6) interactive pedagogical strategies such as an open-source online learning system. Conclusions We developed an innovative healthy public policy (HPP) curriculum in order to support an existing community of practice among public health staff. Dynamic pedagogical strategies and a more formal HPP curriculum can support competence development among public health staff, and this can be achieved while relying mostly on in-house expertise. This constitutes a stimulating capacity-building initiative for the enhancement of public health core competences. Key messages Developing healthy public policy is a core practical competence expected of public health professionals. Yet, organizational practices in building healthy public policy capacity are seldom documented. Developing dynamic pedagogical strategies and a more formal healthy public policy curriculum can support competence development among public health staff, while relying mostly on in-house expertise.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".