The patient and clinician experience of informed consent for surgery: a systematic review of the qualitative evidence
Bibliographic record
Abstract
BACKGROUND: Informed consent is an integral component of good medical practice. Many researchers have investigated measures to improve the quality of informed consent, but it is not clear which techniques work best and why. To address this problem, we propose developing a core outcome set (COS) to evaluate interventions designed to improve the consent process for surgery in adult patients with capacity. Part of this process involves reviewing existing research that has reported what is important to patients and doctors in the informed consent process. METHODS: This qualitative synthesis comprises four phases: identification of published papers and determining their relevance; appraisal of the quality of the papers; identification and summary of the key findings from each paper while determining the definitiveness of each finding against the primary data; comparison of key themes between papers such that findings are linked across studies. RESULTS: Searches of bibliographic databases returned 11,073 titles. Of these, 16 studies met the inclusion criteria. Studies were published between 1996 and 2016 and included a total of 367 patients and 74 health care providers. Thirteen studies collected data using in-depth interviews and constant comparison was the most common means of qualitative analysis. A total of 94 findings were extracted from the primary papers and divided into 17 categories and ultimately 6 synthesised findings related to: patient characteristics, knowledge, communication, the model patient, trust and decision making. CONCLUSIONS: This qualitative meta-aggregation is the first to examine the issue of informed consent for surgery. It has revealed several outcomes deemed important to capture by patients and clinicians when evaluating the quality of a consent process. Some of these outcomes have not been examined previously in research comparing methods for informed consent. This review is an important step in the development of a COS to evaluate interventions designed to improve the consent process for surgery. REGISTRATION: The study protocol was registered on the international prospective register for systematic reviews (PROSPERO ID: CRD42017077101).
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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 | 0.186 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".