Bibliographic record
Abstract
OBJECTIVE: Resource allocation in local public health (LPH) has been reported as a significant challenge for practitioners and a Public Health Services and Systems Research priority. Ensuring available resources have maximum impact on community health and maintaining public confidence in the resource allocation process are key challenges. A popular strategy in health care settings to address these challenges is Program Budgeting and Marginal Analysis (PBMA). This case study used PBMA in an LPH setting to examine its appropriateness and utility. DESIGN: The criteria-based resource allocation process PBMA was implemented to guide the development of annual organizational budget in an attempt to maximize the impact of agency resources. Senior leaders and managers were surveyed postimplementation regarding process facilitators, challenges, and successes. SETTING: Canada's largest autonomous LPH agency. RESULTS: PBMA was used to shift 3.4% of the agency budget from lower-impact areas (through 34 specific disinvestments) to higher-impact areas (26 specific reinvestments). Senior leaders and managers validated the process as a useful approach for improving the public health impact of agency resources. However, they also reported the process may have decreased frontline staff confidence in senior leadership. CONCLUSIONS: In this case study, PBMA was used successfully to reallocate a sizable portion of an LPH agency's budget toward higher-impact activities. PBMA warrants further study as a tool to support optimal resource allocation in LPH settings.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".