Patient in health scale validation and self-management among Omani patients with mental health problems
Bibliographic record
Abstract
The literature contains insufficient data relating to different approaches used by adults for managing their mental disorders, particularly in Oman. Most of the studies have been conducted in Western parts of the world, focusing on managing physical chronic illnesses. Therefore, this study aimed to assess adult Omani mentally ill patients' self-management behavior and to evaluate the validity and reliability of the Arabic version of the Partners in Health (PIH) Scale. Quantitative data were collected from 246 eligible adult participants who were diagnosed with different mental health illnesses from Oman's largest psychiatric and mental health hospital- Al Masarra Hospital. Data were collected using 12 items on the Arabic-translated PIH scale. While descriptive data analysis was used for quantitative data, content validity, and Cronbach's alpha coefficient were used to measure the validity and reliability of the PIH scale, respectively. Results showed that the Arabic version of PIH is valid (I-CVI was greater than 0.79 for all items, and S-CVI was found to be higher than 0.80) and reliable (0.92); Omani mentally ill patients have moderate self-management behavior in general, but specifically, they showed a lower level of knowledge and recognition of the symptoms and management of their mental illness. However, they also showed a moderate adherence to treatment and coping with the disease. It is concluded that the Arabic valid and reliable PIH can be used to measure self-management behavior among the mentally ill Arab population. Healthcare providers in Oman are recommended to integrate health literacy, self-management assessment, and education in their clinical care. Keywords: Partners in health; mental health; self-management; mental illness; instrumentation testing
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 | 0.006 | 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 | 0.001 | 0.000 |
| 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".