Hospice care in Calgary
Bibliographic record
Abstract
Objective To explore Calgary family physicians’ knowledge about hospices, their attitudes toward the referral process, and their understanding of barriers to referral for hospice care. Design Surveys were mailed to 400 randomly selected participants. The survey contained 18 questions related to hospice care, physician experience, attitudes, and perceived barriers to making a hospice referral. Setting Calgary, Alta. Participants Family physicians. Main outcome measures Survey responses were analyzed quantitatively using the ![Graphic][1] 2 goodness-of-fit test, Kruskal-Wallis tests, and logistic regression analyses to examine univariate associations. Qualitative analysis of open-ended questions was done by content analysis and thematic coding. Results In total, 104 surveys were mailed back. Family physicians agreed that palliative care in a hospice setting can greatly improve quality of life for patients, but only 2 of 6 knowledge questions about hospice care were answered correctly by most. Family physicians with special areas of interest or subspecialties were more likely to feel well-informed about hospice referrals ( P = .017), indicated a higher comfort level discussing hospice and palliative care ( P = .030), and were less likely to defer discussing it with patients ( P = .023). Physicians with a special interest in palliative medicine were more likely to correctly answer the knowledge questions ( P < .034) and to be familiar with the referral process ( P < .001), patient eligibility ( P < .001), and the palliative home care program ( P = .003). Qualitative analysis revealed support for palliative home care and consultation services but concerns about caregiver coping and family issues. Concerns about disengagement of family physicians and uncertainty about the referral process are obstacles to referral. Conclusion While Calgary family physicians are appreciative of hospice care, there are knowledge gaps. It is important to engage family physicians in the referral process. [1]: /embed/inline-graphic-1.gif
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".