Screening for psychosocial risk in caregivers of children with medical complexity during the COVID-19 pandemic: a cross-sectional study
Bibliographic record
Abstract
Objective The primary objective was to quantify psychosocial risk in family caregivers (FCs) of children with medical complexity (CMC) during the COVID-19 pandemic using the Psychosocial Assessment Tool (PAT). The secondary objectives were to compare this finding with the average PAT score of this population before the COVID-19 pandemic and to examine potential clinical predictors of psychosocial risk in FCs of CMC. Design Cross-sectional study. Participants FCs of CMC were recruited from the Long-Term Ventilation Clinic at The Hospital for Sick Children, Toronto, Ontario, Canada. A total of 91 completed the demographic and PAT questionnaires online from 10 June 2021 through 13 December 2021. Main outcome measures Mean PAT scores in FCs were categorised as ‘Universal’ low risk, ‘Targeted’ intermediate risk or ‘Clinical’ high risk. The effect of sociodemographic and clinical variables on overall PAT scores was assessed using multiple linear regression analysis. Comparisons with a previous study were made using Mann-Whitney tests and χ 2 analysis. Results Mean (SD) PAT score was 1.34 (0.69). Thirty-one (34%) caregivers were classified as Universal, 43 (47%) as Targeted and 17 (19%) as Clinical. The mean PAT score (1.34) was significantly higher compared with the mean PAT score (1.17) found prior to the COVID-19 pandemic. Multiple linear regression analysis demonstrated an overall significant model, with the number of hospital admissions since the onset of COVID-19 being the only variable associated with the overall PAT score. Conclusion FCs of CMC are experiencing significant psychosocial stress during the COVID-19 pandemic. Timely and effective interventions are warranted to ensure these individuals receive the appropriate support.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".