Fit for purpose? Introducing a rational priority setting approach into a community care setting
Bibliographic record
Abstract
Purpose - Program budgeting and marginal analysis (PBMA) is a priority setting approach that assists decision makers with allocating resources. Previous PBMA work establishes its efficacy and indicates that contextual factors complicate priority setting, which can hamper PBMA effectiveness. The purpose of this paper is to gain qualitative insight into PBMA effectiveness. Design/methodology/approach - A Canadian case study of PBMA implementation. Data consist of decision-maker interviews pre (n=20), post year-1 (n=12) and post year-2 (n=9) of PBMA to examine perceptions of baseline priority setting practice vis-à-vis desired practice, and perceptions of PBMA usability and acceptability. Findings - Fit emerged as a key theme in determining PBMA effectiveness. Fit herein refers to being of suitable quality and form to meet the intended purposes and needs of the end-users, and includes desirability, acceptability, and usability dimensions. Results confirm decision-maker desire for rational approaches like PBMA. However, most participants indicated that the timing of the exercise and the form in which PBMA was applied were not well-suited for this case study. Participant acceptance of and buy-in to PBMA changed during the study: a leadership change, limited organizational commitment, and concerns with organizational capacity were key barriers to PBMA adoption and thereby effectiveness. Practical implications - These findings suggest that a potential way-forward includes adding a contextual readiness/capacity assessment stage to PBMA, recognizing organizational complexity, and considering incremental adoption of PBMA's approach. Originality/value - These insights help us to better understand and work with priority setting conditions to advance evidence-informed decision making.
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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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".