An exploratory study of interprofessional collaboration in end-of-life decision-making beyond palliative care settings
Bibliographic record
Abstract
As healthcare delivery becomes increasingly interprofessional, it is imperative to identify opportunities for effective collaboration and coordination of care. Drawing on a Canadian qualitative study that adopted a constant comparative method based on the grounded theory approach, we report how healthcare providers' (HCPs) personal experiences and professional roles intersect with system factors in hindering or enhancing their ability to support patients and families in planning for end-of-life (EOL) care. We used a criterion-based sampling strategy and sought HCPs who had direct experience engaging patients and families in complex healthcare decisions on: (1) initiating, withholding, or withdrawing treatment; (2) care planning; and/or (3) discharge planning. Interviews sought to understand what HCPs perceived as individual, (inter)professional, and system factors that might hinder, promote, or enhance support for patients/families. We present four major intersecting themes from in-depth interviews with 28 HCPs across acute, long-term, and community care settings that represent three barriers and one facilitator: discomfort with death and dying, confusion about role responsibility, lack of coordinated care, and importance of interprofessional teamwork. Attending to system power hierarchy, we explore interprofessional strategies to support patients' and families' care experiences and promote team-based decision-making. We recommend an interprofessional team approach to facilitate EOL decision-making across care settings and before death becomes imminent. Increasing educational initiatives and developing tools that focus on interprofessional collaboration may help HCPs to understand each other's roles and perspectives, so that they can work together to provide a more coherent and coordinated approach to EOL decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".