Bibliographic record
Abstract
Background Advance care planning (ACP) has the potential to address patients’ end-of-life care needs. In Hong Kong, ACP is relatively new to the public. Older adults and patients with progressive and life-limiting illnesses are beginning to embrace the concept. As such, frontline healthcare professionals, need to have a good understanding of the concepts and the skills to initiate such conversation. The end-of-life care capacity-building program of the CUHK Institute of Ageing, is part of the Jockey Club End-of-life Community Care Project since 2015. It has been training healthcare professionals of the public hospitals in the New Territories East Cluster. And since 2022, the training sessions are extended to the Hong Kong West and East Clusters. Although the Hospital Authority has guidelines for clinicians in promoting ACP to the appropriate patients, there are barriers. It is important to have a thorough understanding of the barriers and the facilitating factors to enhance the progression of ACP to meet the patient and family’s end-of-life care needs. Methods A cross-sectional, self-administered survey will be conducted on nurses of public hospitals in March 2023. The survey is being promoted through the hospital’s central nursing division. The questionnaire is adapted to the Hong Kong culture from a recently published questionnaire in Canada. Data will be collected in Mar and Apr. The results will be statistically analyzed using SPSS. Results The results of the perceived barriers and enablers will be categorized as clinician, patient, and system factors, rating on a 6-point Likert scale from 0 (a minimal extent) to 6 (an extreme amount), and the enablers by using an open-ended question. Conclusion There are barriers to engaging patients and families in ACP at the clinician, patient, and system levels that could potentially be addressed through the development of multidimensional ACP implementation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".