Investigating information-seeking behaviors of primary care physicians who care for older depressed patients and their family caregivers: a pilot study
Bibliographic record
Abstract
Objective To describe preliminary findings from a study of information-seeking behaviors of primary care physicians who care for elderly and depressed patients, and the correlation between what is sought versus what is provided to the patient and (or) caregiver. Setting Physicians in two large ambulatory primary care practices throughout urban Pittsburgh, Pennsylvania, who take care of geriatric patients. Methods Structured interviews, with common questions, will be conducted with 12 primary care physicians to determine patterns of information-seeking behaviors. Environmental scans of physicians' offices for evidence of their existing information behaviors will complement the information obtained from the interviews. Results This pilot study provides an analysis of the resources primary care physicians use to seek information to provide to patients and caregivers. Analyses show types of information sought, time spent seeking information, and methods used to find information given to patients. Conclusions With mounting evidence of the Internet being used for patient self care, it is essential to understand if primary care physicians understand the scope and breadth of information readily available to their patients. The primary care physician needs to be aware of the types of information made available to their patients and the caregivers who are inclined to obtain information for the patient.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".