Evaluating stress experienced by caregivers of children with special health care needs via biomarkers: A systematic review
Bibliographic record
Abstract
BACKGROUND: The body of literature on physiological measures of stress in caregivers of children with special health care needs (CSHCN) is emerging; however, a nondisease-based review of this literature has not yet been conducted. This study aimed to synthesize and analyze scientific evidence available in the literature on biomarkers associated with stress in caregivers of CSHCN. METHODS: We conducted a systematic review of studies published in 7 electronic bibliographic databases: Embase, MEDLINE/PubMed, Cochrane Library, Web of Science, CINAHL, Scopus, and PsycINFO, with no publication data restrictions. The internal validity and risk of bias of the RCT were assessed using the RoB 2 tool, and for NRCTs, the ROBINS-I was employed. The Newcastle-Ottawa Scale was used to evaluate the internal validity of case-control studies and the JBI tool was used to evaluate cross-sectional studies. RESULTS: The search identified 755 papers, and 7 articles were selected and included in the analysis. The included studies were conducted across diverse geographic regions: 3 in North America (the United States and Canada), 2 in Europe (the United Kingdom and Croatia), 1 in Brazil, and 1 in South Korea, demonstrating a degree of international representation. Most of the studies (n = 3; 42,8%) were experimental (RCT or NRCT). Regarding critical appraisal, the majority (n = 5; 71.42%) were considered to be of high quality and presented a low risk of bias through specific tools by study design, although 2 were classified as having a high risk of bias. Cortisol was analyzed in all studies, whereas alpha-amylase was measured in only one study, predominantly using saliva samples. Studies have indicated a tendency for lower cortisol levels upon awakening among caregivers of CSHCN children compared to caregivers of healthy children. CONCLUSIONS: This review suggests that there are important differences between caregivers of CSHCN and healthy children regarding biomarker measures. Biomarkers enable objective measurement of stress and may complement self-report measures that are more commonly used in studies of family caregiving. This review underscores the importance of systematically assessing caregivers' needs in clinical practice and supports the development of public policies and future research initiatives that incorporate biomarker analysis.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".