A survey of mental health literacy in parents and guardians of teenagers
Bibliographic record
Abstract
Introduction: Parents and guardians (hereafter caregivers) of teenagers need high levels of mental health literacy (MHL) to manage mental health problems arising in teenagers in their care. Previous studies assessing MHL levels in caregivers of teenagers have reported mixed results, making it difficult to clearly estimate caregiver MHL levels. This study aimed to investigate MHL levels in Japanese caregivers of regular teenagers. Methods: Responses from caregivers (n = 1,397) of students entering junior and senior high schools to a self-administered online questionnaire were analyzed. The questionnaire assessed (a) knowledge about mental health/illnesses and (b) attitudes towards mental health problems in teens in their care (e.g., recognition of depression as a medical illness and intention to engage in helping behaviors). Results: The average proportion of correct answers to the knowledge questions (n = 7) was 55.4%; about one tenth (9.2%) of caregivers correctly answered only one or none of the questions. Few caregivers correctly answered about the life-time prevalence of any mental illnesses (46.1%) and appropriate sleep duration for teenagers' health (16.5%). The proportions of caregivers who had the intention to listen to the teen in their care, consult another person, and seek professional medical help if the teen suffered from depression were 99.5%, 91.5% and 72.7%, respectively. Conclusions: Many teenagers' caregivers appeared to be willing to help the teens in their care if they were suffering from mental health problems. However, there was much room for improvement in knowledge on mental health/illnesses and intention to seek help from medical professionals. Efforts toward better education should be made.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".