Bibliographic record
Abstract
RATIONALE: Health promotion is where clinical practice and prevention science intersect to address complex or 'wicked' problems that have multiple sources and require a broad perspective to address. This means focusing on the social determinants of health and the complex individual, community and environmental interactions that influence health and wellbeing. Health promotion research and practice recognizes that social change is not linear and involves multiple communities of interest working together in a coordinated manner in order to address health problems. An approach that acknowledges this non-linear system of interaction in its data gathering, strategic planning, and program implementation is necessary to addressing this complexity in practice. METHODS: Concepts such as chaos theory, self-organization, social emergence can inform how health promotion is practiced at multiple levels. Evaluation approaches such as social network analysis, system dynamics modeling combined with social organizing strategies like communities of practice and unconferences provide opportunities to leverage social capital effectively to promote health in complex environments with diverse populations. CONCLUSION: Health promotion's focus on the multi-layered, complex interactions that create or limit health and wellbeing require knowledge and action that match this complexity. Approaches to engagement and evaluation that are based on systems theories and methodologies provide the means of addressing this complexity, while framing health promotion as a systems science and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.063 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".