Social participation experiences of mothers of children with cerebral palsy in an Iranian context
Bibliographic record
Abstract
BACKGROUND/AIM: Social participation is increasingly of interest in research that investigates the impact of caring for a child with a disability. Little has been investigated about the social participation experiences of mothers of children with cerebral palsy (CP). This study explored social participation among Iranian mothers of children with CP. METHODS: The conventional qualitative content analysis method was utilised. Data were collected via in-depth semi-structured interviews with 14 mothers (aged 26-45 years) of children with CP with the gross motor function classification system expanded & revised levels III-V. Constant comparative analysis was deployed for data analysis. RESULTS: The results were identified and classified into three main themes: (i) polarisation of positive and negative feelings; (ii) challenges to mothers' social activity; and (iii) striving to engage in society. CONCLUSION: In the Iranian context, mothers of children with CP are facing many challenges to social participation and seem to have been neglected by the health-care system. One of the priorities of Iranian health policy makers may be developing, establishing and implementing social support to enable the mothers to participate in social activities. Furthermore, occupational therapists can contribute and guide mothers' social participation by creating programs to develop and utilise skills for them.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".