Understanding the reasons to avoid seeking mental health professionals: Validation of the MITOS-MENTAL questionnaire in Peru population
Bibliographic record
Abstract
There is still much resistance, myths, beliefs, and misconceptions regarding the seeking of mental health services for diagnosis and treatment. The objective was to validate an instrument to determine why Peruvian workers would not seek mental health professionals. In an instrumental study, literature was searched, and mental health professionals were asked about the most common reasons for not attending consultations. An expert panel undertook exploratory and confirmatory factor analyses (CFA), which were applied to a large population. Descriptive and instrumental statistics were used for the data. The 20 experts gave excellent ratings to the initial questions. In the pilot (250 people), it was confirmed that all questions had saturations >0.40. The item modification technique was also performed, eliminating six questions. With the CFA in 1312 respondents, it was seen that the goodness-of-fit indices were not adequate for three questions, then the index modification technique was used, achieving a satisfactory factorial structure model (χ2 = 61.497; df = 9; p < 0.001; RMR = 0.015; TLI = 0.984; CFI = 0.990, and RMSEA = 0.067). A scale of six questions was validated to measure the most important reasons why Peruvian workers do not want to attend mental health consultations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".