A study protocol to develop and test an e‐health intervention in follow‐up service for intensive care survivors' relatives
Bibliographic record
Abstract
BACKGROUND: The negative impact on long-term health-related outcomes among relatives of critically ill patients in the intensive care unit (ICU) has been well described. High-quality ICU specialized follow-up care, which is easily accessible with digital innovation and which is designed by and with relevant stakeholders (i.e., ICU patients' relatives and nurses), should be considered to reduce these impairments in the psychological and social domains. AIM: The programme's aim is to develop and test an e-health intervention in a follow-up service to support ICU patients' relatives. Here, the protocol for the overall study programme will be described. STUDY DESIGN: The overall study comprises a mixed-methods, multicentre research design with qualitative and quantitative study parts. The study population is ICU patients' adult relatives and ICU nurses. The main outcomes are the experiences of these stakeholders with the newly developed e-health intervention. There will be no predefined selection based on age, gender, and level of education to maximize diversity throughout the study programme. After the participants provide informed consent, data will be gathered through focus groups (n = 5) among relatives and individual interviews (n = 20) among nurses exploring the needs and priorities of a digital follow-up service. The findings will be explored further for priority considerations among members of the patient/relative organization (aiming n = 150), which will serve as a basis for digital prototypes of the e-health intervention. Assessment of the intervention will be followed during an iterative process with investigator-developed questionnaires. Finally, symptoms of anxiety and depression will be measured with the 14-item Dutch version of the 'Hospital Anxiety and Depression Scale', and symptoms of posttraumatic stress will be measured with the 21-item Dutch version of the 'Impact of Events Scale-Revised' to indicate the effectiveness of digital support among ICU patients' relatives. RELEVANCE TO CLINICAL PRACTICE: The e-health intervention to be developed during this research programme can possibly bridge the gap in integrated ICU follow-up care by providing relevant information, self-monitoring and stimulating self-care among ICU patients' relatives.
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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 | 0.063 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.020 |
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".