Barriers facing family physicians providing palliative care service in Hong Kong: a questionnaire survey
Bibliographic record
Abstract
Objective: To investigate willingness and barriers for family physicians to provide palliative care service in Hong Kong. \nDesign: A combined qualitative and quantitative research method. \nSubjects: All local members of the Hong Kong College of Family Physicians (HKCFP). \nMain outcome measures: Demographic data, ideas and factors concerning provision of palliative care in Hong Kong. Generalised Estimating Equations (GEE) model and Logistic Regression analysis to determine factors affecting doctor’s wish and the actual provision of palliative care in practice. \nResults: Overall, 750 (48.1%) responses from respondents with a similar distribution in age and gender profile as our target population were returned. General barriers identified were time concern and not enough support from various disciplines. Specific barriers affecting actual provision of service were knowledge and experience (p<0.001), problems dealing with death (p=0.013), current public-private interface (p=0.016) and cultural concerns (p=0.022). Having an interest (p=0.002), continuity of care (p<0.001), patient needs (p<0.001), having a specialist qualification(p=0.009) and primary qualification obtained in Canada (p=0.001) were found to be supporting factors for willingness and actually providing palliative care in their practice. \nConclusion: The factors and suggestions learned from this study should be addressed if collaboration between palliative care and primary care is considered for community palliative care service in Hong Kong. Further studies focusing on patients and their family members’ perspectives are essential to understand the actual need in our cultural context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".