The Caregiver Pathway, a Model for the Systematic and Individualized Follow-up of Family Caregivers at Intensive Care Units: Development Study
Bibliographic record
Abstract
BACKGROUND: Family caregivers of patients who are critically ill have a high prevalence of short- and long-term symptoms, such as fatigue, anxiety, depression, symptoms of posttraumatic stress, and complicated grief. These adverse consequences following a loved one's admission to an intensive care unit (ICU) are also known as post-intensive care syndrome-family. Approaches such as family-centered care provide recommendations for improving the care of patients and families, but models for family caregiver follow-up are often lacking. OBJECTIVE: This study aims to develop a model for structuring and individualizing the follow-up of family caregivers of patients who are critically ill, starting from the patients' ICU admission to after their discharge or death. METHODS: The model was developed through a participatory co-design approach using a 2-phased iterative process. First, the preparation phase included a meeting with stakeholders (n=4) for organizational anchoring and planning, a literature search, and interviews with former family caregivers (n=8). In the subsequent development phase, the model was iteratively created through workshops with stakeholders (n=10) and user testing with former family caregivers (n=4) and experienced ICU nurses (n=11). RESULTS: The interviews revealed how being present with the patient and receiving adequate information and emotional care were highly important for family caregivers at an ICU. The literature search underlined the overwhelming and uncertain situation for the family caregivers and identified recommendations for follow-up. On the basis of these recommendations and findings from the interviews, workshops, and user testing, The Caregiver Pathway model was developed, encompassing 4 steps: within the first few days of the patient's ICU stay, the family caregivers will be offered to complete a digital assessment tool mapping their needs and challenges, followed by a conversation with an ICU nurse; when the patient leaves the ICU, a card containing information and support will be handed out to the family caregivers; shortly after the ICU stay, family caregivers will be offered a discharge conversation by phone, focusing on how they are doing and whether they have any questions or concerns; and within 3 months after the ICU stay, an individual follow-up conversation will be offered. Family caregivers will be invited to talk about memories from the ICU and reflect upon the ICU stay, and they will also be able to talk about their current situation and receive information about relevant support. CONCLUSIONS: This study illustrates how existing evidence and stakeholder input can be combined to create a model for family caregiver follow-up at an ICU. The Caregiver Pathway can help ICU nurses improve family caregiver follow-up and aid in promoting family-centered care, potentially also being transferrable to other types of family caregiver follow-up.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".