Assessing the general mental health of special educators of handicap and special needs children
Bibliographic record
Abstract
Background: The handicap and disabilities among individuals are a global problem with high numbers among them. The study aimed to detect symptoms of psychological problems among special educators in governmental institutes of handicap children at Al-Najaf City.Methodology: A descriptive analytic study was conducted in governmental institutes of handicap children in Al-Najaf City. The study sample included all special needs teachers in governmental institutes (38 teachers) teachers who had a licensed to dealing with handicap children. The study instruments had two parts. First part included the demographic characteristics of teachers like age, gender, residence, faculty, academic year, and socioeconomic data. Second part had the general health questionnaire 12-items to detect their general mental health in non-psychiatric persons by self-reported. Result: The study revealed most of special educators are worked in Al-Noor institute for blindness (32%) from all educators are participated in the study and most of them are a female (81.2%) with aged more than 35 years old. The majority (73.68%) of general health state of special educators is a good general health and residual ratio about (26%) is rated as a poor general health state.Conclusion: The study concluded the number of special educators in government handicap institutes is a few and most of them are not specialist. About one third of educators were a female and their income was insufficient. Also, one quarter had a poor mental health so the study recommended to support and train them about how deal with stresses and other psychological problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".