Design and implementation of a survey of senior Canadian healthcare decision-makers: Organization-wide resource allocation processes
Bibliographic record
Abstract
A three-year research project based in British Columbia, Canada, is attempting to develop a framework and tools to assist healthcare system decision-makers achieve “high performance” in resource allocation. In pursuit of this objective, a literature search was conducted and two phases of primary data collection are being undertaken: an online survey of senior healthcare decision-makers, and in-depth case studies of potential “high performing” organizations. This paper addresses the survey phase; our aim is to provide a practical example of the mechanics of survey design, of benefit to those who want to better understand our forthcoming results, but also as an aid to other researchers grappling with the hard choices and trade-offs involved in the survey development process. Survey content is described in light of the existing literature, with discussion of the choices made by the research team to decide what questions and items would be included and excluded. The target population for the survey was senior managers in Canadian regional health authorities (or the closest equivalent organizations) in each of the 10 provinces and 3 territories. The paper dis- cusses how this sample was obtained, and describes the survey implementation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".