Determinants of utilization and out-of-pocket payments for psychiatric healthcare in Iran
Bibliographic record
Abstract
Purpose Mental health is an inevitable and vital dimension when it comes to providing a global definition for the appropriate health status. This highlights the importance of investigating factors influencing utilization and out-of-pocket payments (OOP) for mental health services. Thus, the purpose of this paper is to assess the determinants of the utilization and OOP for psychiatric healthcare in Iran. Design/methodology/approach A total of 39,864 households were included in this cross-sectional study. Data on the utilization and OOP for psychiatric healthcare as well as all their determinants (e.g. wealth index of households, geographical area, household size, etc.) were extracted from the Household Income and Expenditure Survey (HIES). The HIES was conducted by the Statistical Center of Iran in 2016. A zero-inflated Tobit model was used to identify the main factors affecting utilization and OOP for psychiatric healthcare utilization. Findings The average of utilization and OOP for psychiatric services was found to be 14.67 times per 1,000 households and $7.783 per month for service users, respectively. There were significant positive relationships between income and utilization (p=0.0002) and OOP (p<0.0001) for psychiatric services. Significant negative associations were found between the number of illiterate people in the household and OOP (coefficient=−1.56) and utilization (coefficient=−0.2002) for psychiatric services. Utilization and OOP for psychiatric services were statistically significantly higher among households with higher wealth status. Originality/value Despite the higher rate of mental disorders, the utilization of psychiatric services in Iran is very low. Due to financial barriers and insufficient insurance coverage, high socioeconomic status (SES) households utilize more psychiatric services than low-SES households. Thus, the integration of mental health services in public health programs is required to improve the utilization of psychiatric services in Iran.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".