Multidisciplinarity in health promotion: a bibliometric analysis of current research
Bibliographic record
Abstract
Health promotion (HP) is a relatively recent field that stems from, notably, public health, sociology, political science, psychology and education. This multidisciplinarity has contributed to HP's challenged institutionalization. Scholars have so far predominately explored HP's multidisciplinarity using anecdotal approaches, limiting our understanding of the breadth and interplay of the disciplines constituting HP research. The overall aim of this paper is to contribute to a better understanding of HP's multidisciplinarity using a bibliometric approach. We developed a three-pronged approach: (i) we examined the most cited journals within Health Promotion International; (ii) we asked an international panel of HP scholars (n = 27) to vote on the journals most relevant to their work; (iii) we examined the most common words in article abstracts among journals which received the highest number of votes. We used multiple correspondence analyses to examine similarities between HPI references, scholars' votes and abstracts' words. We found evidence that HP research reached across numerous disciplines but segregated into distinct subgroups with conflicting perspectives. We found that HPI was the only journal that was identified as relevant by a majority (81% of participants). Multidisciplinarity is a key feature of HP. It can strengthen HP by enriching our understanding of health and social issues from a variety of perspectives, but it may also divide experts into disciplinary silos. This may ultimately weaken its institutional pathways and its contribution to public health. More academic venues and institutions should be developed to facilitate collaboration among HP scholars and practitioners.
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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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.209 | 0.305 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".