Can a Discrete Choice Experiment contribute to person-centred healthcare?
Bibliographic record
Abstract
In person-centred decision making the relative importance of the considerations that matter to the person is elicited and combined, at the point of decision, with the best estimates available on the performance of the available options on those criteria. Whatever procedure is used to implement this in a clinical decision, average preferences emerging from group or subgroup research cannot contribute directly, since they can have only a statistical relationship with the preferences of the individual person. The precise relationship is knowable by eliciting those of the individual concerned, but there would be little point consulting the averages if this is done. A scan of recent Discrete Choice Experiment (DCE) publications reveals frequent claims that the group-level results can somehow contribute to, or facilitate, better clinical decision making. Typically there are only vague or ambiguous indications of how this could happen, the ambiguity often arising from the use and positioning of the apostrophe in the words persons and patients. Only when the person opts out of preference provision and asks to be treated as ‘average’, can the results of a DCE have clinical relevance in genuinely person-centred healthcare. One cannot derive an ought from an is and one cannot derive an I from a they. DCE researchers should refrain from implying that their results could, let alone should, have any impact on person-centred clinical decisions. Group-level DCE results are clearly conceptually appropriate for health system or service decisions, but the suggestion that they have clinical relevance is a serious deterrent to the development and provision of effective means of individual preference elicitation and specification at the point of decision. Those who wish to foster person-centred care should be alert to the dangers of claims based on group-level analyses such as DCEs.
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 | 0.608 | 0.708 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".