Polarity in healthcare priority setting: a comparative analysis of attitudes among Finnish physicians, dentists, MPs, and citizens
Bibliographic record
Abstract
BACKGROUND: Our objective is to study the differences and similarities in public healthcare priority setting attitudes among Finnish physicians, dentists, members of the parliament (MPs), and citizens. Additionally, we explore the correlation of socio-demographic factors with attitudes among the citizens. A comprehensive study including the views of all relevant stakeholder groups has been lacking, and the attitudes of dentists and parliamentarians have not been studied previously. METHODS: Our study design employs a cross-sectional survey and comparative analysis to examine the attitudes of citizens, physicians, dentists, and MPs. The online attitude surveys were conducted in Finland between January and March 2022. The survey sample consisted of 1,500 physicians (240 answered), 500 dentists (177), 200 MPs (27) and 2,794 citizens (1,001). Using Pandas for cross-tabulation and the Pearson Chi-square test for significance at a 95% confidence level, we compared attitudes across stakeholder groups and demographic variables like age, gender, and income. We measured the strength of relationships using Cramer’s V, considering only effects larger than 0.10 to be meaningful. RESULTS: We found significant differences in priority setting attitudes among physicians, dentists, MPs, and citizens. Physicians and dentists were more accepting, while citizens had a more negative attitude. In terms of specific priority setting principles, the most significant differences between stakeholder groups were related to views on individuals’ health behaviour. The study also revealed differences between physicians and dentists. Citizens, especially more vulnerable groups, had a more negative attitude towards priority setting in general compared to specific principles. Age, education, labour market position and income had a weak but significant association with citizens’ attitudes towards priority setting. CONCLUSIONS: We found that citizens’ attitudes towards prioritising and rationing health services differ significantly from those of healthcare professionals and politicians. The results emphasise the complex nature of the priority setting debate and draw attention to how different groups are involved in healthcare decision-making and methods used for their inclusion. The findings encourage facilitating discussions among stakeholders to inform them of the realities and advantages of priority setting, thereby increasing its acceptability and legitimacy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".