Patient activation and support needs in patients after ICU discharge: A UK survey of critical illness survivors
Bibliographic record
Abstract
Background: Understanding the degree to which patients are actively involved, confident and capable of engaging with self-management and rehabilitation could be an initial step in guiding individualised supportive strategies for people after critical illness. Aims: To assess the levels of active involvement with self management among ICU survivors using the Patient Activation Measure (PAM), explore associations between patient characteristics and PAM results, and investigate its relationship with patients’ support needs at key transition points during the recovery process. Methods: Eligible participants received both the PAM and Support Needs After Critical care (SNAC) questionnaires by post. The return of the completed questionnaires was considered as consent to participate. Ethical approval was obtained (17/NI/0236). Descriptive statistics were used to summarise the data and Pearson’s coefficient for correlations between variables. Findings: There were 200 completed PAM and SNAC questionnaires. PAM scores showed that levels of active involvement with self management fell into level 1 ( n = 64; disengaged and overwhelmed, low confidence to self manage) and 2 ( n = 70; still struggling), with considerably less participants achieving scores in level 3 ( n = 51; taking action) and 4 ( n = 15; pushing further). Lower patient activation levels were associated with higher support needs (r = −0.16, p = 0.02). Conclusion: We found that patient activation levels are low implying low knowledge, skills and confidence to self-manage after critical illness, and also that patients have support needs at various timepoints during recovery. Future research should focus on a longitudinal study to track changes in activation and support needs in the same patients over time and identify effective strategies to optimise recovery after critical illness.
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How this classification was reachedexpand
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".