How to measure barriers in accessing mental healthcare? Psychometric evaluation of a screening tool in parents of children with intellectual and developmental disabilities
Bibliographic record
Abstract
Abstract Caring for children with special needs can cause an enormous physical and emotional burden, and therefore these parents have an elevated risk to experience mental health problems. The characteristics of current healthcare systems and parents’ responsibilities to care for their children seem to impede their access to mental healthcare. There is so far a lack of instruments to screen for such obstacles. The aim of this study was to develop and validate a scale for measuring barriers to accessing mental healthcare. The Parental Healthcare Barriers Scale (PHBS) was developed on the basis of an extensive literature research, input and discussion from experts and parents with lived experience. A cross-sectional survey was used to collect data from 456 parents of children with intellectual and developmental disabilities (IDD). Physical health, mental health, social support, and parenting were measured for convergent and discriminant validity of the PHBS. The PHBS scale revealed acceptable to good reliability and validity. It consists of four subscales (i.e., support accessibility, personal belief, emotional readiness, and resource availability). The PHBS found parents prioritized their children’s treatments over their own mental health challenges (93.4%), did not have enough time (90.4%), and had financial concerns (85.8%). Parents in rural and remote areas had more limited resources. Findings from our study suggest increasing the public literacy on mental health challenges, introducing evidence-based treatments, increasing the availability of healthcare services for parents, and adjusting current services to their needs.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".