Supporting and resourcing treatment decision‐making: some policy considerations
Bibliographic record
Abstract
This paper considers some of the policy implications of issues raised during a conference about treatment decision-making in the clinical encounter held in Hamilton, Ontario in May 1999. Policies promoting patient participation in treatment decision-making need to be flexible enough to ensure that they are appropriate across the range of contexts in which health care decisions are made and acceptable to people with diverse preferences and abilities. They should also be formulated in consideration of other health policies and of available resources. Policies of informing people and involving them in decisions about their care are unlikely to be simple to implement. Various strategies might be needed to support them. These include the development of appropriate skills among health professionals and in the general population, the use of interventions to encourage people to play more active roles in decisions about their health care, the provision of decision aids for people facing specific decisions and the provision and accreditation of more general information resources and services. If information and other facilitators of patient participation in decision-making are seen as integral to good quality health care, then funding should be made available for them. This will, however, have opportunity costs. Policy makers' decisions about how much health care funding should be invested in which strategies should be underpinned by good research evidence about the effects that different types of intervention have on a range of outcomes for individuals, health care systems and populations. The knowledge on which current policies are based is limited. The development of future policies will be enhanced if policy makers invest in critical conceptual thinking, reflective practice, imaginative development work and good quality evaluative research.
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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 | 0.150 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.031 | 0.038 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.065 | 0.038 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".