In-depth mixed-method case study to assess how to support and communicate with the families of hospitalised patients during COVID-19: a social innovation embedded in clinical teams
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study is to describe and evaluate, in a real-life context, the support and communicate with families (SCF) team's contribution to maintaining communication and supporting relatives when patients are at the end of their lives by mobilising the points of view of SCF team members, healthcare professionals, managers and the relatives themselves. DESIGN: An in-depth mixed-method case study (quantitative and qualitative). Individual interviews were conducted with members of the SCF team to assess the activities and areas for improvement and with co-managers of active COVID-19 units. Healthcare professionals and managers completed a questionnaire to assess the contribution made by the SCF team. Hospitalised patients' relatives completed a questionnaire on their experience with the SCF team. SETTING: The study was conducted in a university teaching hospital in the province of Québec, Canada. PARTICIPANTS: Members of the SCF team, healthcare professionals, managers and relatives of hospitalised patients. RESULTS: Between April and July 2020, 131 telephone communications with families and healthcare professionals, 43 support sessions for relatives of end-of-life patients and 35 therapeutic humanitarian visits were carried out by members of the SCF team. Team members felt that they had played an active role in humanising care. Fully 83.1% of the healthcare professionals and managers reported that the SCF team's work had met the relatives' needs, while 15.1% believed that the SCF team should be maintained after the pandemic. Fully 95% of the relatives appreciated receiving the telephone calls and visits, while 82% felt that the visits had positive effects on hospitalised patients. CONCLUSION: The COVID-19 pandemic forced the introduction of a social innovation involving support for and communication with families. The intention of this innovation was to support the complexity of highly emotional situations experienced by families during the COVID-19 pandemic.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".