Challenges to evidence-based health promotion: a case study of a Food Security Coalition in Ontario, Canada
Bibliographic record
Abstract
Developing the evidence base for health promotion can be challenging because interventions often have to target competing determinants of health, including social, structural, environmental and political determinants; all of which are difficult to measure and thus evaluate. Drawing on a case study of food insecurity, which refers to inadequate access to food due to financial constraints, we illustrate the challenges faced by community-based organizations in collecting data to form an evidence base for the development and evaluation of collective programmes aimed at addressing food insecurity. Interviews were conducted with members of a multi-stakeholder coalition (n = 22 interviewees; n = 10 organizations) who collectively work to address food insecurity in their community through a range of community-based programmes and services. Member organizations also provided a list of measures currently used to inform programme and service development and evaluation. Data were collected in a city in Southern Ontario, Canada between May and September 2015. Participants identified four barriers to collecting data: Organizational needs and philosophies; concerns surrounding clientele wellbeing and dignity; issues of feasibility; and restrictive requirements imposed by funding bodies. Participants also discussed their previous successes in collecting meaningful data for identifying impact. Our results point to the challenge of generating data suitable for developing and evaluating programmes aimed at broader determinants of health, while maintaining the primary goal of meeting clients' needs. Documenting change at intermediate- and macro-levels would provide evidence for the collective effectiveness of current programmes and services offered. However, appropriate resources need to be invested to allow for scientific evaluation.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".