Picturing ELSI+: a visual representation of ethical, legal, and social issues, and patient experiences in Health Technology Assessment in Canada
Bibliographic record
Abstract
OBJECTIVES: Consideration of ethical, legal, and social issues plus patient values (ELSI+) in health technology assessment (HTA) is challenging because of a lack of conceptual clarity and the multi-disciplinary nature of ELSI+. We used concept mapping to identify key concepts and inter-relationships in the ELSI+ domain and provide a conceptual framework for consideration of ELSI+ in HTA. METHODS: We conducted a scoping review (Medline and EMBASE, 2000-2016) to identify ELSI+ issues in the HTA literature. Items from the scoping review and an expert brainstorming session were consolidated into eighty ELSI+-related statements, which were entered into Concept Systems® Global MAX™ software. Participants (N = 38; 36 percent worked as researchers, 21 percent as academics; 42 percent self-identified as HTA experts) sorted the statements into thematic groups, and rated them on importance in making decisions about adopting technologies in Canada, from 1 (not at all important) to 5 (extremely important). We used Concept Systems® Global MAX™ software to create and analyze concept maps with four to sixteen clusters. RESULTS: Our final ELSI+ map consisted of five clusters, with each cluster representing a different concept and the statements within each cluster representing the same concept. Based on the concepts, we named these clusters: patient preferences/experiences, patient quality of life/function, patient burden/harm, fairness, and organizational. The highest mean importance ratings were for the statements in the patient burden/harm (3.82) and organizational (3.92) clusters. CONCLUSIONS: This study suggests an alternative approach to ELSI+, based on conceptual coherence rather than academic disciplines. This will provide a foundation for incorporating ELSI+ into HTA.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".