Minding the gap: Prioritization of care issues among nurse practitioners, family physicians and geriatricians when caring for the elderly
Bibliographic record
Abstract
Accumulating health problems of the elderly requires recognition of geriatric syndromes, while shifting away from a conventional disease-specific approach. We surveyed 179 practitioners representing Family Physicians (FPs), Nurse Practitioners (NPs) and geriatricians in Ontario, in order to quantify how they prioritize syndromes, diseases and conditions in the elderly. Identifying differences may inform opportunities for interprofessional sharing of expertise among professionals pursuing a common goal, which is expected to improve interprofessional collaboration. Our survey (response rate 36%) identifies that NP, FP and geriatrician respondents all recognize co-occurrence of "multiple morbidities" as one of the most frequently encountered issues when serving the elderly, however FPs and NPs place higher priority on managing individual chronic diseases than explicitly prioritizing geriatric syndromes. Our findings identify a need for a more clearly defined role for the geriatrician as syndrome-educator and implies further need for collaborative approaches to caring for seniors that values different professional's expertise.
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.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".