Bibliographic record
Abstract
This study considers three questions: 1. What are the Canadian public's prioritization preferences for new government spending on a range of public health-related goods outside the scope of the country's national system of health insurance? 2. How homogenous or heterogeneous is the Canadian public in terms of these preferences? 3. What factors are predictive of the Canadian public's preferences for new government spending? Data were collected in 2008 from a national random sample of Canadian adults through a telephone interview survey (n=1,005). Respondents were asked to rank five spending priorities in terms of their preference for new government spending. Bivariate and multivariable logistic regression analyses were conducted. As a first priority, Canadian adults prefer spending on child care (26.2%), followed by pharmacare (23.1%), dental care (20.8%), home care (17.2%), and vision care (12.7%). Sociodemographic characteristics predict spending preferences, based on the social position and needs of respondents. Policy leaders need to give fair consideration to public preferences in priority setting approaches in order to ensure that public health-related goods are distributed in a manner that best suits population needs.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".