Psychometric Properties of the Abdominal Pain Index (API) in the Iranian Adolescent Population
Bibliographic record
Abstract
Considering the high prevalence of abdominal pain in children and adolescents in Iran, it is essential to use appropriate screening tools. One of the most comprehensive, yet concise, tools for this purpose is the Abdominal Pain Index (API). This study aimed to investigate the psychometric properties of the Persian version of the self-report API in adolescents. In this descriptive study, A total of 162 Iranian adolescents in the age range of 12 to 18 years were considered as the sample group, which included two groups of school students (n = 125) and adolescent patients with abdominal pain (n = 37). Clinical sample was selected by the available sampling method, and nonclinical sample was selected by the cluster sampling method. Adolescents in the sample group were selected from both clinical and nonclinical groups in order to evaluate differential validity. Instruments, including API, somatic symptoms subscale of the General Health Questionnaire (GHQ), and McGill Pain Questionnaire (MPQ), were also completed for the participants. Also, to evaluate the construct validity of API, exploratory and confirmatory factor analysis methods were applied. The exploratory factor analysis identified one general factor, and the confirmatory factor analysis results show the model’s satisfactory fitting. Also, the researchers’ hypothesis, i.e., API is a single-factor model (with five items), was approved. The reliability coefficient of the questionnaire was satisfactory for the total scale (α < 0.7). This study showed that API could be used with considerable confidence for Iranian children and adolescents with chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".