Final evaluation of the “Making Health Choices” advance care planning in aged care project
Bibliographic record
Abstract
Published literature suggests that advance care planning (ACP) activity in aged-care homes is inconsistent and often of poor quality. There are few published reports of the effectiveness of formal training and implementation of ACP in aged-care homes. The Respecting Patient Choices Program piloted a training and implementation model in 19 aged-care homes in Victoria, Australia in 2010-11. The model required selected aged-care home nurses and management staff to attend 3 workshops over a 6-month period and complete pre and post-workshop tasks. The workshops included didactic and experiential teaching, discussions and role plays. Model evaluation included ACP documentation audits, in addition to pre and post implementation surveys to examine aged-care home ACP policies and practices, and staff knowledge, attitudes and behaviours around ACP. Key results were as follows: Measurement Pre-training (%) Post-training (%) P value % of staff reporting the existence of written policies on ACP in aged-care homes. 53 100 0.008 % of aged-care homes. which discuss medical treatment options with resident/family 42 100 0.016 % of aged-care homes which discuss “things that matter most about life and living” with resident 8 92 0.009 % of aged-care homes reporting that ACP information is used effectively within the facility. 33 100 0.013 % of staff reporting that ACP is carried out “well/very well” within their aged-care home. 25 100 0.009 Mean staff score on ACP knowledge test [SD] 4.4 [1.1] 5.7 [0.9] 0.001 The available data suggests that the RPC model of ACP in aged-care homes succeeded in training staff and implementing ACP.
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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 | 0.098 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".