People and money matter: investment lessons from the Ontario heart health program, Canada
Bibliographic record
Abstract
Resource allocation is a critical issue for public health decision-makers. Yet little is known about the level and type of resources needed to build capacity to plan and implement comprehensive programs. This paper examines the relationships between investments and changes in organizational capacity and program implementation in the first phase (1998-2003) of the Ontario Heart Health Program (OHHP)--a province-wide, comprehensive public health program that involved 40 community partnerships. The study represents a subset of findings from a provincial evaluation. Investments, organizational capacity of public health units and implementation of heart health activities were measured longitudinally. Investment information was gathered annually from the provincial government, local public health units and community partners using standard reports, and was available from 1998 to 2002. Organizational capacity and program implementation were measured using a written survey, completed by all health units at five measurement times from 1994 to 2002. Combining provincial and local sources, the average total investment by year five was $1.66 per capita. Organizational capacity of public health units and implementation of heart health activities increased both before and during the first 2 years of the OHHP, and then plateaued at a modest level for capacity and a low level for implementation after that. Amount of funding was positively associated with organizational capacity, yet this association was overpowered by the negative influence of turnover of a key staff position. Regression analysis indicated that staff turnover explained 23% of local variability in organizational capacity. Findings reinforce the need for adequate investment and retention of key staff positions in complex partnership programs. Better accounting of public health investments, including monetary and in-kind investments, is needed to inform decisions about the amount and duration of public health investments that will lead to effective program implementation.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".