A severely fragmented concept: Uncovering citizens’ subjective accounts of severity of illness
Bibliographic record
Abstract
Universal healthcare is constrained by national governments' finite health resources. This gives rise to complex priority-setting dilemmas. In several universal healthcare systems, the notion of severity (Norwegian: 'alvorlighet') is a key consideration in priority setting, such that treatments for 'severe' illness may be prioritised even when evidence suggests it would not be as cost-effective as treatment options for other conditions. However, severity is a poorly-defined concept, and there is no consensus on what severity means in the context of healthcare provision - whether viewed from public, academic, or professional perspectives. Though several public preference-elicitation studies demonstrate that severity is considered relevant in healthcare resource distribution, there is a paucity of research on public perceptions on the actual meaning of severity. We conducted a Q-methodology study between February 2021 and March 2022 to investigate views on severity amongst general public participants in Norway. Group interviews (n = 59) were conducted to gather statements for the Q-sort ranking exercises (n = 34). Data were analysed using by-person factor analysis to identify patterns in the statement rankings. We present a rich picture of perspectives on the term 'severity', and identify four different, partly conflicting, views on severity in the Norwegian population, with few areas of consensus. We argue that policymakers ought to be made aware of these differing perspectives on severity, and that there is need for further research on the prevalence of these views and on how they are distributed within populations.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".