[Palliative care: profile of medical practice in the Quebec city region].
Bibliographic record
Abstract
OBJECTIVE: To describe the palliative care provided by physicians in the Quebec city region and to identify factors that affect its delivery. DESIGN: Mailed survey. SETTING: Quebec city region. PARTICIPANTS: General practitioners in active clinical practice. MAIN OUTCOME MEASURES: Physicians' personal and professional characteristics and their palliative care practice (volume of work, source of requests for follow-up care, place of delivery of care, resources used, difficulties, encountered). RESULTS: Of the 476 physicians (67%) who responded to our survey, 295 (62%) provided palliative care. Of these, 70% saw no more than two patients requiring palliative care per month, and 55% devoted no more than 2 hours per week to this aspect of patient care. Most (76%) provided palliative care in a variety of settings (private office, home, institution). Home care teams working out of local community health centres are the resource physicians drew upon most frequently (69%). The main difficulties encountered were a lack of clinical expertise, scheduling home care, and providing patients and families with emotional support. CONCLUSION: Most physicians in the Quebec city region provided palliative care occasionally. This care could be improved by removing various logistical and professional barriers.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".