Priority setting in the provincial health services authority: survey of key decision makers
Bibliographic record
Abstract
BACKGROUND: In recent years, decision makers in Canada and elsewhere have expressed a desire for more explicit, evidence-based approaches to priority setting. To achieve this aim within health care organizations, knowledge of both the organizational context and stakeholder attitudes towards priority setting are required. The current work adds to a limited yet growing body of international literature describing priority setting practices in health organizations. METHODS: A qualitative study was conducted using in-depth, face-to-face interviews with 25 key decision makers of the Provincial Health Services Authority (PHSA) of British Columbia. Major themes and sub-themes were identified through content analysis. RESULTS: Priorities were described by decision makers as being set in an ad hoc manner, with resources generally allocated along historical lines. Participants identified the Strategic Plan and a strong research base as strengths of the organization. The main areas for improvement were a desire to have a more transparent process for priority setting, a need to develop a culture which supports explicit priority setting, and a focus on fairness in decision making. Barriers to an explicit allocation process included the challenge of providing specialized services for disparate patient groups, and a lack of formal training in priority setting amongst decision makers. CONCLUSION: This study identified factors important to understanding organizational context and informed next steps for explicit priority setting for a provincial health authority. While the PHSA is unique in its organizational structure in Canada, lessons about priority setting should be transferable to other contexts.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".