‘We’ not ‘I’: health advocacy is a team sport
Bibliographic record
Abstract
CONTEXT: Health advocacy, although recognised as a professional responsibility, is often seen as overwhelming, perhaps because it is framed conceptually as an activity that each physician should undertake alone rather than as a collaborative process. In the context of a study exploring how effective physician health advocates conceptualise their roles and their activities related to health advocacy, we uncovered data that speak directly of the issue of whether the activities of health advocates are enacted as individual or collective pursuits. METHODS: We interviewed ten physicians, identified by others as effective health advocates, regarding their advocacy activities. We collected and analysed data in an iterative process, informed by constructivist grounded theory, continuously refining the interview framework and examining evolving themes. The final coding scheme was applied to all transcripts. RESULTS: Health advocacy was viewed by these physicians as a collective activity. This collective construction of advocacy presented in three ways: (i) as teamwork by interprofessional teams of individuals with clearly defined roles and functional, task-oriented goals; (ii) as a process involving networks of resources or people that can be accessed for both support and reinforcement, and (iii) as a process involving collaborative think-tanks in which members contribute different perspectives to enact collective problem solving at a conceptual level. CONCLUSIONS: Effective health advocates do not conceptualise themselves as stand-alone experts who must do everything themselves. Their collective approach makes it possible for these physicians to incorporate health advocacy into their clinical practice. However, although conceptualising health advocacy as a collective activity may make it less daunting, this way of understanding health advocacy is not compatible with current formal descriptions of the associated competencies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".