Using photovoice to engage underserved children with neurodevelopmental disorders and their caregivers in health research: a mixed methods systematic review
Bibliographic record
Abstract
Introduction: Limited guidance exists for researchers wanting to use photovoice to engage children with neurodevelopmental disorders (NDDs), 0-25 years, and their caregivers in health research. This mixed-methods systematic review synthesized photovoice research with this population with attention to children and caregivers from diverse backgrounds. Diversity of study participants, research areas that have used photovoice with this population, feasibility considerations (adaptations, contextual considerations, practicality), and recommendations provided by study authors were of interest. Methods: We searched five databases and limited the search to English or French language publications. Eighteen studies met the inclusion criteria. We used a convergent integrated synthesis approach as well as qualitative content analysis to synthesize data from included studies. Results: = 3). Photovoice has been used across six research areas relevant to individual, interpersonal, and organizational level influences on an individual's life. Authors of selected studies faced various contextual considerations (e.g., requiring flexibility) and made adaptations (e.g., using smiley/sad faces to monitor assent) to facilitate research participation. Authors reported photovoice as valuable and useful and provided implementation recommendations (e.g., work one-on-one with participants) and future research directions (e.g., using photovoice with nonverbal children) to advance the use of this methodology. Discussion: Our findings support using photovoice to explore the lived experience of this population, provide guidance to health and rehabilitation researchers seeking inclusive, person-centred approaches to engaging participants in research, and have direct implications for practice. Systematic Review Registration: https://osf.io/3xsak/.
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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 | 0.071 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".