What should be publicly funded in the Colombian health system? A mixed methods study of citizens’ perceptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In recent years, citizens have become more interested and willing to influence health policy decision-making, and governments worldwide are more prone to citizen engagement in such processes. Prioritising which health technologies should be publicly funded is one decision that requires prudence and consideration of the values and expectations of the people who will be affected by it. OBJECTIVE: To identify and understand the citizens' perceptions about which health technologies should be publicly funded in Colombia. DESIGN: Sequential exploratory mixed methods study; the first was a qualitative embedded case study, and the second was a Q methodology study. PARTICIPANTS: 46 citizens were interviewed, and 30 citizens ordered a Q-sample of 45 statements. ANALYSIS: Interviews were content analysed. We performed a content analysis of the interviews, and, for the quantitative strand, we performed a principal component analysis and varimax rotation to identify view patterns. We also estimated the z-scores of each statement and the load to each factor. We jointly interpreted both sets of findings. RESULTS: We identified two general approaches citizens used to consider public funding of healthcare technologies. One approach endorsed full coverage of all health technologies required by every Colombian. In the second approach, public funding is conditional on the characteristics of the person who needs the technology, their disease/condition, the kind of technology required and the expectation of efficient health system performance. When integrating the results of the Q methodology, we found five patterns of points of view about the public funding of health technologies. CONCLUSION: Colombian citizens consider and balance a range of different factors when making decisions about which health technologies are publicly funded. Citizens not only use technical criteria to decide but also provide the perspective and values of those affected by the decision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it