Understanding Patient and Family Perspectives of Accelerated Discharge Planning in the Critically Ill
Bibliographic record
Abstract
Accelerated discharge, which involves initiation of discharge planning earlier in patients’ intensive care unit (ICU) stay, has become more common to reduce discharge delays and mitigate capacity strain challenges experienced in Canadian ICUs. Successful accelerated discharge planning relies on effective clinician communication and partnering with patients and family caregivers, who are the only constant throughout the care journey. To best enable family caregivers to feel self-efficacious in collaborating in the care of their critically ill loved one, their perspectives on the practice of accelerated discharge planning are needed to help facilitate successful transitions in care. The work presented in this thesis sought to understand patient and family perspectives on accelerated discharge planning as with their insights on supportive elements in accelerated discharge plans. We conducted 45-minute virtual semi-structured interviews with former critically ill patients and family caregivers of former critically ill patients between October 2021 and January 2022. We used inductive, reflexive thematic analysis to identify relevant themes and subthemes. Key themes identified following thematic analysis from seven participants (n=2 former critically ill patients, n=5 family caregivers) included: discharge planning process and stakeholder goal alignment (i.e., benefits of earlier planning, communication and continuity of care, and desire for stakeholder collaboration in care), patient and family support needs in accelerated discharge planning (i.e., informational support, psychological support, and logistical support), scope of support across care settings (i.e., form and timing of supports), individuality of dyads (i.e., condition, capacity, and environmental characteristics specific to each dyad), facilitators and barriers to accelerated discharge planning at the individual and structural level. The work presented in this thesis suggest that patients and families are receptive to accelerated discharge planning because accelerated discharge planning may provide opportunities for patients and family that are not present in current models of discharge; that is, there is alignment of objectives between this transition in care model and patient and family goals of care. Individualized, realistic accelerated discharge plans that provide informational, psychological, and logistical supports may help facilitate successful transitions in care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".