Perspectives on which health settings geriatricians should staff: a qualitative study of patients, care providers and health administrators
Bibliographic record
Abstract
BACKGROUND: With a shortage of geriatricians and an aging population, strategies are needed to optimise the distribution of geriatricians across different healthcare settings (acute care, rehabilitation and community clinics). The perspectives of knowledge users on staffing geriatricians in different healthcare settings are unknown. We aimed to understand the acceptability and feasibility (including barriers and facilitators) of implementing a geriatrician-led comprehensive geriatric assessment (CGA) in acute care, rehabilitation, and community clinic settings. METHODS: A qualitative description approach was used to explore the experience of those implementing (administrative staff), providing (healthcare providers), and receiving (patients/family caregivers) a geriatrician-led CGA in acute care, rehabilitation and community settings. Semi-structured interviews were conducted in Toronto, Canada. The theoretical domains framework and consolidated framework for implementation research informed the interview guide development. Analysis was conducted using a thematic approach. RESULTS: Of the 27 participants (8 patients/caregivers, 9 physicians, 10 administrators), the mean age was 53 years and 14 participants (52%) identified as a woman (13 [48%] identified as a man). CGAs were generally perceived as acceptable but there was a divergence in opinion about which healthcare setting was most important for geriatricians to staff. Acute care was reported to be most important by some because no other care provider has the intersection of acute medicine skills with geriatric training. Others reported that community clinics were most important to manage geriatric syndromes before hospitalization was necessary. The rehabilitation setting appeared to be viewed as important but as a secondary setting. Facilitators to implementing a geriatrician-led CGA included (i) a multidisciplinary team, (ii) better integration with primary care, (iii) a good electronic patient record system, and (iv) innovative ways to identify patients most in need of a CGA. Barriers to implementing a geriatrician-led CGA included (i) lack of resources or administrative support, (ii) limited team building, and (iii) consultative model where recommendations were made but not implemented. CONCLUSIONS: Overall, participants found CGAs acceptable yet had different preferences of which setting to prioritise staffing if there was a shortage of geriatricians. The main barriers to implementing the geriatrician-led CGA related to lack of resources. CLINICAL TRIAL NUMBER: Not applicable.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".