Fatigue after CriTical illness (FACT): Co-production of a self-management intervention to support people with fatigue after critical illness
Bibliographic record
Abstract
PURPOSE: Fatigue is a common and debilitating problem in patients recovering from critical illness. To address a lack of evidence-based interventions for people with fatigue after critical illness, we co-produced a self-management intervention based on self-regulation theory. This article reports the development and initial user testing of the co-produced intervention. METHODS: We conducted three workshops with people experiencing fatigue after critical illness, family members, and healthcare professionals to develop a first draft of the FACT intervention, designed in web and electronic document formats. User testing and interviews were conducted with four people with fatigue after critical illness. Modifications were made based on the findings. RESULTS: Participants found FACT acceptable and easy to use, and the content provided useful strategies to manage fatigue. The final draft intervention includes four key topics: (1) about fatigue which discusses the common characteristics of fatigue after critical illness; (2) managing your energy with the 5 Ps (priorities, pacing, planning, permission, position); (3) strategies for everyday life (covering physical activity; home life; leisure and relationships; work, study, and finances; thoughts and feelings; sleep and eating); and (4) goal setting and making plans. All material is presented as written text, videos, and supplementary infographics. FACT includes calls with a facilitator but can also be used independently. CONCLUSIONS: FACT is a theory driven intervention co-produced by patient, carer and clinical stakeholders and is based on contemporary available evidence. Its development illustrates the benefits of stakeholder involvement to ensure interventions are informed by user needs. Further testing is needed to establish the feasibility and acceptability of FACT. IMPLICATIONS FOR CLINICAL PRACTICE: The FACT intervention shows promise as a self-management tool for people with fatigue after critical illness. It has the potential to provide education and strategies to patients at the point of discharge and follow-up.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".