Development and Psychometric Evaluation of a Measure of Mental Health Literacy in Parents of Adolescents.
Bibliographic record
Abstract
Background: Parents play an essential role in helping an adolescent who has a mental health concern; however, there are no measures of parental mental health literacy for parents of adolescents. Few measures of mental health literacy assess the underlying components of the construct, and measures that assess facets of mental health literacy in parents are limited in psychometric quality. Objective: To develop and psychometrically evaluate a theoretically informed measure of parental mental health literacy, the Parental Mental Health Literacy (ParM-Lit) scale. Method: The ParM-Lit was developed through the generation of items across key domains, expert review of items, and parental feedback. Parents of adolescents (N = 698) completed an online survey including the ParM-Lit and measures of parental attitudes toward help-seeking and knowledge of mental health disorders. Results: Exploratory and confirmatory factor analyses supported a 4- and 5-factor model; however, a 31-item, 4-factor model showed slightly superior fit. Internal consistency of the overall ParM-Lit scale was very good (α = .89), and test-retest reliability was moderate (ICC = .68; 95% CI = .61-.75). The ParM-Lit was strongly associated with parental attitudes towards help-seeking and moderately associated with knowledge of mental health disorders. Conclusions: The ParM-Lit is the first measure of parental mental health literacy, and our findings support its psychometric properties. This final 31-item measure holds promise for advancing measurement of parental mental health literacy in clinical and research settings.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".