Fair reckoning: a qualitative investigation of responses to an economic health resource allocation survey
Bibliographic record
Abstract
OBJECTIVE: To investigate how participants in an economic resource allocation survey construct notions of fairness. DESIGN: Qualitative interview study guided by interpretive grounded theory methods. SETTING AND PARTICIPANTS: Qualitative interviews were conducted with volunteer university- (n=39) and community-based (n =7) economic survey participants. INTERVENTION OR MAIN VARIABLES STUDIED: We explored how participants constructed meanings to guide or explain fair survey choices, focusing on rationales, imagery and additional desired information not provided in the survey scenarios. MAIN OUTCOME MEASURES: Data were transcribed and coded into qualitative categories. Analysis iterated with data collection iterated through three waves of interviews. RESULTS: Participants compared the survey dilemmas to domains outside the health system. Most compared them with other micro-level, inter-personal sharing tasks. Participants raised several fairness-relevant factors beyond need or capacity to benefit. These included age, weight, poverty, access to other options and personal responsibility for illness; illness duration, curability or seriousness; life expectancy; possibilities for sharing; awareness of other's needs; and ability to explain allocations to those affected. They also articulated a fairness principle little considered by equity theories: that everybody must get something and nobody should get nothing. DISCUSSION AND CONCLUSIONS: Lay criteria for judging fairness are myriad. Simple scenarios may be used to investigate lay commitments to abstract principles. Although principles are the focus of analysis and inference, participants may solve simplified dilemmas by imputing extraneous features to the problem or applying unanticipated principles. These possibilities should be taken into account in the design of resource allocation surveys eliciting the views of the public.
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 | 0.082 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".