Committed, ambivalent, concealed, or distanced: community organisations’ perceptions of their role in local prevention systems
Bibliographic record
Abstract
Kathleen P. Conteab* , Tayhla J. Ryderac, Liza Hopkinsad, Maria Gomeza & Therese Rileyaea The Australian Prevention Partnership Centre, Ultimo, Australiab School of Public Health, DePaul University, Chicago, Illinois, USA; Menzies Centre for Health Policy and University Centre for Rural Health, School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, Australiac Australian Institute of Health Innovation, Macquarie University, Sydney, Australiad Alfred Health, Melbourne, Australiae Therese Riley Consulting, Melbourne, AustraliaCONTACT Kathleen P. Conte Kathleen.conte@sydney.edu.auABSTRACTPrevention of chronic diseases happens within systems of action across whole communities. In local communities, organisations play important roles that influence health, but many are in non-health sectors where health or ‘prevention’ is not their main remit. In this paper, we explore how organisations in communities perceive their role as part of a local system of chronic disease prevention. We use interview data from ‘Prevention Tracker’, an investigation undertaken with Australian communities that aimed to describe how local prevention systems are organised. Four communities participated – one regional, one remote, and two urban. In each, local advisory groups identified key informants for semi-structured interviews (n = 80). We applied the Ottawa Charter for Health Promotion as a sensitising framework to categorise responses; and undertook inductive thematic analysis to generate deeper insights into organisations’ orientation to prevention. While some respondents clearly recognised and articulated linkages between their organisation’s activities and prevention, sometimes drawing from health promotion principles to do so, many did not and were prompted by the interview to consider their contributions. A proportion explicitly distanced themselves from prevention despite their work directly addressing the social determinants that underscore health. For some, this distancing reflects a tactical decision to reduce competition and promote partnership between organisations, and to engage clients. This study demonstrates that diverse organisations make contributions to prevention – often outside of ‘core business’ – whether or not the organisations themselves realise, relate to and/or self-identify as playing this role.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".