Information Needs and Information-Seeking Behavior of Primary Care Physicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine the information needs of primary care physicians in Spain and to describe their information-seeking patterns. METHODS: This observational study took place in primary care practices located in Madrid, Spain. Participants were a random stratified sample of 112 primary care physicians. Physicians' consultations were video recorded for 4 hours. Clinical questions arising during the patient visit and the sources of information used within the consultation to answer questions were identified. Physicians with unanswered questions were followed up by telephone 2 weeks later to determine whether their questions had since been answered and the sources of information used. Clinical questions were classified by topic and type of information. RESULTS: A total of 3,511 patient consultations (mean length, 7.8 minutes) were recorded, leading to 635 clinical questions (0.18 questions per consultation). The most frequent questions were related to diagnosis (53%) and treatment (26%). The most frequent generic type of questions was "What is the cause of symptom x?" (20.5%). Physicians searched for answers to 22.8% of the questions (9.6% during consultations). The time taken and the success rate in finding an answer during a consultation and afterward were 2 minutes (100%) and 32 minutes (75%), respectively. CONCLUSIONS: Primary care physicians working in settings where consultations are of short duration have time to answer only 1 in 5 of their questions. Better methods are needed to provide answers to questions that arise in office practice in settings where average consultation time is less than 10 minutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it