Federal and Provincial Governments Need to Be Transparent about Trade-offs When They Buy Healthcare
Bibliographic record
Abstract
S pending on healthcare is carefully scrutinized by the public, the media and academics because the amounts are so large and represent a very significant proportion of provincial budgets.Some quarters are calling for increases in spending, whereas others are focused on restraint owing to perceived inefficiencies and ineffectiveness.The debate over healthcare spending has continued for decades and is likely to heat up as new provincial labour agreements have locked in annual healthcare spending increases of at least five percent for 2023 (BC Nurses' Union 2023; ONA 2023).Putting aside increases in taxes or borrowing, the principle of public spending is simple: the budget of available funds that support social programs, healthcare, education and transportation infrastructure is fixed.Politicians then make choices allocating available funds to the budgets of individual programs.Through this process, healthcare has been a perennial winner at the cost of other programs receiving less funding than sought.If governments allocate available funding to programs or infrastructure that provides more value than all other alternatives, the province will have achieved the most with its public funds.In other words, the loss to the province and its residents from opportunities not funded is the smallest when budgets are allocated toward programs that generate the largest value.This is the foundational concept of opportunity cost in the field of health economics.The same is true within programs.Allocating healthcare funding to programs that generate the most health realizes the highest possible value for the public spending.Ideally, these budget allocation decisions are informed by evidence such as clinical effectiveness, patients' and clinicians' perspectives and ethical practices.Health economics plays a major role in generating this evidence using the field' s tools to calculate value for money and whose outputs include cost per quality-adjusted life-year (QALY). Finding the Highest Value for Public SpendingAs two healthcare policy researchers who actively partner with decision makers to improve Federal and Provincial Governments Need To Be Transparent about Trade-Offs When They Buy Healthcare E D I TO R I A L This issue' s final research manuscript by Mathews et al. ( 2023) uses a qualitative study design to untangle the factors associated with limiting COVID-19 exposure among family physicians' practices.The authors report that family physicians received too little and irrelevant practice-specific support from provincial public health authorities that instead emphasized acute care.The authors conclude that mass assessment and testing centres would improve performance in future influenza-like pandemics.
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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.031 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".