Physician house calls – a solution for the elderly?
Bibliographic record
Abstract
Physician house calls are a unique modality of care directed towards the frail and chronically ill elderly who are otherwise unable to access care. Home visits are a re-emerging form of healthcare delivery with the aim to minimise re-hospitalisations and support positive ageing at home strategies. The United Kingdom, Denmark and Australia have established national policies, while various localities in the United States, Canada and Europe have piloted preventive house calls projects, some of which have shown a reduction in unnecessary emergency room visits and hospitalisations. Inevitably, as individuals age, disability and morbidity increases. It is becoming increasingly important to find cost-effective solutions to quality care as the costs of healthcare continue to rise. Although house calls offer many benefits and may be rewarding experiences for both the physician and patient, low financial compensation and the time-consuming nature of the visit may deter physicians from providing this type of care in Ireland. Without the provision of house calls, an unmet need for geriatric care evolves. Further economic evaluation is required to determine if house calls are a financially sound solution. In considering house calls, a multidisciplinary approach to physician home visits in Ireland should also be explored, as this modality may deliver more comprehensive and efficient care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".