Validity and Reliability of the Persian Versions of Primary and Secondary Screening Instrument for Targeting Educational Risk Questionnaires
Bibliographic record
Abstract
Background and Aim: Hearing loss in children leads to speech and language delays, low academic achievement, literacy delays, and psychosocial difficulties. Screening instrument for targeting educational risk (SIFTER) is one of the questionnaires used for evaluation of students’ performance in schools. The current study aims to develop Persian versions of primary and secondary SIFTER questionnaires and assessing their validity and reliability. Methods: The main English versions of primary and secondary SIFTER questionnaires were translated into Persian named as P-SIFTER and secondary P-SIFTER. Then, their face validities were determined based on the options of related experts. The final versions were completed by 55 teachers of 150 students (64 primary and 86 secondary school students) divided into two groups of hearing-impaired (HI) and normal-hearing (NH) students. The test- retest reliabilities were assessed in 117 students (64 primary and 53 secondary school students). Results: The results revealed that these questionnaires had high face validity. The content validity index for P-SIFTER and secondary P-SIFTER were obtained 0.94 and 0.92, respectively. The total score of P-SIFTER was 51.85 and 65.41 in HI and NH students, respectively. For the secondary P-SIFTER, it was 58.75 and 67.48, respectively. The test-retest reliability showed high correlation for NH and HI students between P-SIFTER and secondary P-SIFTER scores. The Cronbach’s alpha value for the overall score of P-SIFTER was 0.96 for both HI and NH students; for secondary P-SIFTER, the values were 0.94 and 0.93, respectively. Conclusion: The Persian versions of primary and secondary SIFTER questionnaires have acceptable validity and reliability.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".