3 themes described what involvement in treatment decision making meant to patients with diabetesCommentary
Bibliographic record
Abstract
V Entwistle Dr V Entwistle, Universities of Dundee & St Andrews, Dundee, Scotland, UK; v.a.entwistle@dundee.ac.uk What does being involved in treatment decision making mean to patients with diabetes? Qualitative study. 4 multipractitioner outpatient diabetes clinics in Scotland, UK. 7 adults with type 1 diabetes and 11 with type 2 diabetes (age range 20–79 y, 56% men). Patients participated in semi-structured interviews ⩽1 week after an outpatient visit. Interviews addressed patients’ experiences with, and feelings about, involvement in treatment decision making. Interviews were audiotaped and transcribed, and data were analysed thematically. All patients had an understanding of involvement, and most comments focused on decision making about treatment for which health professionals were the gate keepers. Issues that patients associated with involvement in treatment decision making were grouped into 3 broad, overlapping themes. (1) Ethos and feel of healthcare encounters . Patients associated several professional behaviours with involvement, including being friendly and welcoming, “taking an active …
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".