An environmental scan of online resources for informal family caregivers of ICU survivors
Bibliographic record
Abstract
PURPOSE: To collate a comprehensive repository of online resources for family caregivers of intensive care survivors to inform a recovery website and digital peer support programme. MATERIALS AND METHODS: To identify resources, we conducted an environmental scan using processes recommended by the Canadian Agency for Drugs and Technologies in Health and guided by clinical experts, former patients, and family members. We searched internet sources, professional society websites, social media, and contacted our professional networks. RESULTS: Through expert consultation we identified 16 information categories and found 301 online resources. Five categories with the most resources were: how to look after yourself/recognise anxiety or post-traumatic stress/getting mental health support (n = 63); information specific to conditions necessitating ICU admission (n = 49); multiple category resources (n = 46); symptoms of post-intensive care syndrome (n = 44); stories of lived experience (n = 23). Five categories with the least resources were physical, emotional and cognitive symptoms of post-intensive care syndrome-family (n = 1); interacting with primary care (n = 2); medical deterioration (how to recognise/what to do) (n = 2); driving and accessing the community (n = 3); end-of-life and bereavement (n = 5). Of these resources, we included 45 on our recovery website. CONCLUSION: This environmental scan identifies multiple resources addressing informational needs of family caregivers and highlights areas for resource development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".