Twenty-five years of programme budgeting and marginal analysis in the health sector, 1974-1999
Bibliographic record
Abstract
OBJECTIVES: Programme budgeting and marginal analysis (PBMA) is a priority setting framework that has been used over the last 25 years in the health sector. To date, no formal evaluation of this framework has been conducted. The aims of this study were to categorize previous PBMA exercises systematically and determine the impact of PBMA in regional health authorities (RHAs) internationally. METHODS: Thirty authors of grey literature and published papers on PBMA, and an additional six economists with research interests in PBMA, were surveyed with a mailed questionnaire. Previous exercises were categorized and details of the short- and long-term impacts of the framework were obtained. Descriptive statistics and content analysis were used. RESULTS: The PBMA framework was identified as having been used 78 times in 59 RHAs. For the exercises where longer-term impact was known, the approach was viewed as having had a positive impact, as defined by the setting of priorities or shifting of resources, in 59% of cases and continued to be used in at least 52% of the RHAs. The primary reasons why PBMA was discontinued included personnel changes and lack of internal 'champions'. CONCLUSIONS: Contrary to popular perception, there has been widespread diffusion of PBMA in RHAs internationally and, overall, the impact of this approach has been positive. Although there is general agreement on the validity of the economic principles underlying PBMA, addressing managerial issues would seem to be central to successful implementation in a given region.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".