Revisiting the Do-Live-Well Health Promotion Framework: A Citation Content Analysis
Bibliographic record
Abstract
Background. The Do-Live-Well (DLW) framework was first published in 2015 and aimed to fill a theoretical gap in the health promotion literature related to the links between occupational patterns and health. However, the extent of uptake and use of the framework since publication is unknown. Purpose. To explore and reflect on the adoption and application of DLW in the literature. Method. Citation content analysis of two seminal DLW publications was conducted from 2015 to November 2022 across six databases. Findings. Seventeen citations directly applied DLW to inform research ( n = 10), practice ( n = 5) and knowledge translation ( n = 2). Implications. The findings highlight uptake of the framework in a range of settings, and how it can inform an occupation-based understanding of health and well-being. Ongoing knowledge dissemination, development of practice tools, and research to update evidence and examine relevance are needed to further advance the utility and application of the framework.
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 | 0.113 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.113 | 0.133 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".