Must we appear to be all-knowing?: patients' and family physicians' perspectives on information seeking during consultations.
Bibliographic record
Abstract
OBJECTIVE: To compare patients' opinions about family physicians looking up medical information during consultations with family physicians' expectations of how patients would respond to their using sources to find answers to medical questions. DESIGN: Survey. SETTING: North York, Ont. PARTICIPANTS: One hundred fifty-three family practice patients, 54 family physicians, and 21 family practice residents. MAIN OUTCOME MEASURES: Patients' self-reported confidence in their family physicians and their perceptions of the quality of care after seeing physicians look up medical information, both without specifying the physician's source of information and with reference to several specific information media. Family physicians' predictions for how patients would respond to their using resources to answer medical questions. RESULTS: When the information source used by physicians was not specified, 9% and 7% of patients reported decreased confidence and perceived lower quality of care, respectively. When the information source used by physicians was specified, the proportions of negative responses for patients' confidence and their perceptions of quality of care were 39% and 31%, respectively, for Internet search engines (ISEs); 8% and 7% for online resources designed for physicians (ORDP); 27% and 27% for personal digital assistants (PDAs); and 10% and 9% for hard-copy medical textbooks (HMTs). When the information source was not specified, 32% and 12% of physicians expected patients to report negative responses for confidence and perceptions of quality of care, respectively. When the information source was specified, 51% and 33% of physicians expected patients to report negative responses for confidence and perceptions of quality of care, respectively, for their use of ISEs; 16% and 8% for ORDP; 20% and 12% for PDAs; and 36% and 21% for HMTs. Younger patients were more likely to respond negatively to physicians' use of resources, especially if the source was an ISE (P<.001). Physicians earlier in their careers were more likely to expect negative patient responses (P<.05). CONCLUSION: Family physicians overestimated the decrease in patients' confidence caused by seeing them look up medical questions. While most patients responded positively, a substantial proportion of younger patients reported decreased confidence. Patients believed the best sources of information were ORDP and HMTs.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".