Validity and reliability of the Mexican resilience measurement scale in families of children with chronic conditions
Bibliographic record
Abstract
BACKGROUND: The resilience to face disease is a process of positive adaptation despite the loss of health. It involves developing vitality and skills to overcome the negative effects of adversity, risks, and vulnerability caused by disease. In Mexico, the Mexican Resilience Measurement Scale (RESI-M) has been validated with a general population and has a five-factor structure. However, this scale does not allow evaluation of resilience in specific subpopulations, such as caregivers. METHOD: This study investigated the psychometric properties of RESI-M in 446 family caregivers of children with chronic diseases. A confirmatory factor analysis (CFA) was performed, internal consistency values were calculated using Cronbach's alpha coefficient, and mean comparisons were determined using t-tests. RESULTS: The expected five-factor model showed an adequate fit with the data based on a maximum likelihood test. The internal consistency for each factor ranged from .76 to .93, and the global internal consistency was .95. No average difference in RESI-M and its factors was found between women and men. CONCLUSION: The RESI-M showed internal consistency and its model of five correlated factors was valid among family caregivers of children with chronic diseases.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".