156 Impact of healthcare professionals’ training in advance care planning on the family caregivers of patients with serious illnesses burden: secondary analysis of a randomized clustered comparative effectiveness study
Bibliographic record
Abstract
Introduction Advance Care Planning (ACP) remains essential for patients with serious illnesses. The multicomponent program named Serious Illness Care Program (SICP) suggests two approaches of ACP, that is, individual or interprofessional. A previous study indicated the value of both approaches for improving the quality of care provided to patients with serious illnesses through increased implementation of shared decision- making. Here, we compare how they impact the care burden of family caregivers of patients with serious illnesses. Methods We conducted a secondary analysis of a clustered randomized controlled trial from USA and Canada. We use Consort guideline to report our study. Two groups of professionals were trained in two different SICP approaches. Patients with serious illnesses cared by each group were then recruited to refer their family caregivers. We adapted a 12-item questionnaire from the 22-item Zarit Burden Interview to measure the care burden of family caregivers. We used the Palette conceptual framework to assess factors influencing this care burden. Patient partners from both USA and Canada were actively involved through steering meetings. Results We included 192 family caregivers in our study. Most of them are female (67.7%) and live with the patient (63%). There are no statistically significant differences in the perceived level of caregiver burden between the two groups (p-value = 0.43). Discussion The lack of statistically significant differences between the two groups could be justified by the absence of a module on family caregivers in the training. It would, therefore, be important to incorporate this aspect into healthcare professionals’ training to ensure their involvement in shared decision-making processes. Conclusion Family caregivers play an active role in the care process of patients with serious illnesses and should be involved in decisions regarding the patient.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".