A Novel Approach to Study Medical Decision Making in the Clinical Setting: The “Own‐point‐of‐view” Perspective
Bibliographic record
Abstract
BACKGROUND: Making diagnostic and therapeutic decisions is a critical activity among physicians. It relies on the ability of physicians to use cognitive processes and specific knowledge in the context of a clinical reasoning. This ability is a core competency in physicians, especially in the field of emergency medicine where the rate of diagnostic errors is high. Studies that explore medical decision making in an authentic setting are increasing significantly. They are based on the use of qualitative methods that are applied at two separate times: 1) a video recording of the subject's actual activity in an authentic setting and 2) an interview with the subject, supported by the video recording. Traditionally, activity is recorded from an "external perspective"; i.e., a camera is positioned in the room in which the consultation takes place. This approach has many limits, both technical and with respect to the validity of the data collected. OBJECTIVES: The article aims at 1) describing how decision making is currently being studied, especially from a qualitative standpoint, and the reasons why new methods are needed, and 2) reporting how we used an original, innovative approach to study decision making in the field of emergency medicine and findings from these studies to guide further the use of this method. The method consists in recording the subject's activity from his own point of view, by fixing a microcamera on his temple or the branch of his glasses. An interview is then held on the basis of this recording, so that the subject being interviewed can relive the situation, to facilitate the explanation of his reasoning with respect to his decisions and actions. RESULTS: We describe how this method has been used successfully in investigating medical decision making in emergency medicine. We provide details on how to use it optimally, taking into account the constraints associated with the practice of emergency medicine and the benefits in the study of clinical reasoning. CONCLUSION: The "own-point-of-view" video technique is a promising method to study clinical decision making in emergency medicine. It is a powerful tool to stimulate recall and help physicians make their reasoning explicit, thanks to a greater psychological immersion.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".