“There’s Huge Value in Knowing What’s Going On”: a Mixed Methods Study to Explore Geriatricians’ Perspectives on Best Practices for Information Transfer Between Hospital-Based Geriatricians and Primary Care Physicians
Bibliographic record
Abstract
Background: Geriatricians' work provides holistic recommendations to improve the health of older adults, considering medical, social, psychological, and functional domains. Their implementation most often relies on primary care physicians. Extant evidence suggests benefit from systematized information transfer between hospital-based specialists and primary care physicians. Yet, direct communication between hospitals and primary care physicians is rare. We aimed to describe the information transfer practice of hospital-based geriatricians in Quebec, Canada. Methods: We sent a survey to all (146) geriatricians and Geriatric Medicine residents of Quebec on their current practice and opinions on information transfer and obtained 64 responses. We then performed 20-minute semi-structured interviews with 13 participants to further explore knowledge on information transfer, barriers and facilitators, risks and benefits, and recommendations to improve transmission. Results: While geriatricians believe that their recommendations should be transmitted to primary care physicians and that the absence of a systematic information transfer procedure has a negative impact on quality of care, only 1.6% report having such a procedure in place in their practice. They think that the absence of information transfer procedures disrupts the communications of key diagnoses and medication changes, and leads to duplicated interventions. Harnessing technology to facilitate information transfer is viewed as a solution. Conclusion: Information transfer between hospital-based geriatricians and primary care physicians in Quebec is rare. The absence of a systematic information transfer procedure is seen by geriatricians as a hindrance to the provision of safe, high-quality care to older adults.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".