Reflexivity in Health Promotion: A Typology for Training
Bibliographic record
Abstract
Reflexivity has emerged as a key concept in the field of health promotion (HP). Yet it remains unclear how diverse forms of reflexivity are specifically relevant to HP concerns, and how these "reflexivities" are interconnected. We argue that frameworks are needed to support more systematic integration of reflexivity in HP training and practice. In this article, we propose a typology of reflexivity in HP to facilitate the understanding of reflexivity in professional training. Drawing from key theories and models of reflexivity, this typology proposes three reflexive positions (ideal-types) with specific purposes for HP: reflexivity in, on, and underlying action. This article illustrates our typology's ideal-types with vignettes collected from HP actors working with reflexivity in North America and Europe. We suggest that our typology constitutes a conceptual device to organize and discuss a variety of experiences of engaging with reflexivity for HP. We propose the typology may support integrating reflexivity as a key feature in training a future cadre of health promoters and as a means for building a responsible HP practice.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".