Informed patients: legal fiction or reality?
Bibliographic record
Abstract
Background: Shared decision making is based on the assumption that it is acceptable, even important and probably favourable to involve patients in the decision-making process. To achieve this goal, patients need to be informed about the content and the aim of the treatment, alternative treatments, possible side-effects and risks. In the Netherlands and Finland, the right of informed consent is protected by the ‘Law on Medical Treatment’. In Belgium such a law secondary is being prepared, while in the United Kingdom and Germany guidelines were developed. Aim: The aim of the presentation is to investigate (1) the importance patients attach to LMT-items; (2) the extent to which general practitioners put the LMT-items into practice; and (3) the contribution of information-giving about medical and therapeutical issues to shared decision making. Methods: Data was derived from the video-observation study being part of the Second Dutch National Survey of General Practice. In 86 practices video-recordings of consultations were made of 142 GPs and 2784 patients. Patients filled in questionnaires before and just after their visit. Doctor-patient communication of 15 patients per GP was rated by observers by means of Roter’s Interactional Analysis System (RIAS), who also registered - among others - data on LMT-items. Only patients of 18 years and older were included (N=1787). Data were analysed by t-tests, Pearson’s correlation coefficient and logistic regression analysis. Results: Patients attach much importance to be informed about the treatment and its side-effects, alternative treatments, and shared decision making (75-90%). However, three quarter of the patients liked to leave the final decision to the GP. In the patients’ eyes, agreement about relevance and performance of the LMT-items was high for treatment information, but less for information about side-effects and alternatives. The (more objective) video-observers, however, rated lower percentages. Many patients who wanted shared decision making were involved indeed, but still 30% was not. In 72% of the consultations the GP took the final decision. Male and lowly educated patients liked to leave the decision to the GP, whereas highly educated patients wanted shared decision making. GPs gave room to and encouraged older patients more than younger ones to shared decision making. Information about medical/therapeutic issues (as rated by RIAS) appeared not to be related to shared decision making (taking into account relevant characteristics). Conclusions: Relevance and performance of LMT-items do not always agree, but perhaps information is not always required or desired. Patients seem to have a positive picture of GPs’ performance of the TML-items. Not all patients want shared decision making, but those who do are often involved in the decision process. More research into the decision making process is recommended. (aut. ref.)
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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 | 0.034 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".