Stakeholder Surveys of Canadian Healthcare Performance: What Are They Telling Us? Who Should Be Listening? Who Should Be Acting, and How?
Bibliographic record
Abstract
Eleven Health Care in Canada (HCIC) surveys, spanning 1998-2014, offer a comprehensive overview of the changing perceptions of physician, nurse, pharmacist, administrator and public stakeholders of the nation's health status, its burden of illness and its quality and cost of care. Overall, there persists a universal sense of quality in our health system--despite evidence that national health status is declining, chronic illnesses are increasing, patients' timely access to care and ability to afford care are diminishing and all these indicators are predicted to worsen over time. Among the public and health professionals, key priorities for improving future patient care are increasing professional schools' output and team-based care, along with enhanced use of national supply systems to reduce costs of care. Among HCIC survey partners, the overarching goal has been, and remains, the utilization of knowledge gained from the surveys to facilitate evidence-driven health policy and improved patient care and outcomes. Practical foci are the development of knowledge translation (KT) activities and assessment of their impact. This paper outlines current initiatives to track reach of member and non-member audiences for HCIC information; to ascertain how they perceive and value the various KT messages, vehicles and metrics; and to potentially identify a hierarchy of efficacy for impact factors. The primary objective is to inform future HCIC survey design and reporting, especially identification of KT vehicles and venues that are most effective in terms of reach and impact in facilitating understanding of, and subsequent action around, the knowledge generated.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".