Public involvement in the priority setting activities of a wait time management initiative: a qualitative case study
Bibliographic record
Abstract
Abstract Background As no health system can afford to provide all possible services and treatments for the people it serves, each system must set priorities. Priority setting decision makers are increasingly involving the public in policy making. This study focuses on public engagement in a key priority setting context that plagues every health system around the world: wait list management. The purpose of this study is to describe and evaluate priority setting for the Ontario Wait Time Strategy, with special attention to public engagement. Methods This study was conducted at the Ontario Wait Time Strategy in Ontario, Canada which is part of a Federal-Territorial-Provincial initiative to improve access and reduce wait times in five areas: cancer, cardiac, sight restoration, joint replacements, and diagnostic imaging. There were two sources of data: (1) over 25 documents (e.g. strategic planning reports, public updates), and (2) 28 one-on-one interviews with informants (e.g. OWTS participants, MOHLTC representatives, clinicians, patient advocates). Analysis used a modified thematic technique in three phases: open coding, axial coding, and evaluation. Results The Ontario Wait Time Strategy partially meets the four conditions of 'accountability for reasonableness'. The public was not directly involved in the priority setting activities of the Ontario Wait Time Strategy. Study participants identified both benefits (supporting the initiative, experts of the lived experience, a publicly funded system and sustainability of the healthcare system) and concerns (personal biases, lack of interest to be involved, time constraints, and level of technicality) for public involvement in the Ontario Wait Time Strategy. Additionally, the participants identified concern for the consequences (sustainability, cannibalism, and a class system) resulting from the Ontario Wait Times Strategy. Conclusion We described and evaluated a wait time management initiative (the Ontario Wait Time Strategy) with special attention to public engagement, and provided a concrete plan to operationalize a strategy for improving public involvement in this, and other, wait time initiatives.
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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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".