Photovoice as a Participatory Research Tool in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
BACKGROUND: Photovoice is a qualitative research tool increasingly utilized in the healthcare field to understand the illness experience from the patient and caregiver perspective. This is the first study to evaluate photovoice in the context of amyotrophic lateral sclerosis (ALS). OBJECTIVE: A patient and caregiver centered research tool was utilized to gain a greater understanding of challenges faced when living with ALS. METHODS: Eight patients and three corresponding caregivers participating by taking photographs, writing descriptive text, and participating in individual and group interviews. Inductive thematic analysis was employed to uncover recurring themes. RESULTS: Five main themes were identified; 1) facing the diagnosis, 2) loss of function, 3) isolation, 4) health system challenges, and 5) hope. Despite the devasting impact of ALS, the majority of participants reported a surprising amount of positivity in the face of receiving this difficult diagnosis, and demonstrated incredible creativity and adaptability to meet the ensuing loss of function. However, patients and caregivers discussed feelings of isolation and health care system challenges. The importance of hope was a strong and recurring theme. CONCLUSIONS: The photovoice research tool demonstrates the profound resilience of these participants, and challenges the medical community to find ways of fostering positivity and hope throughout the ALS disease course. Further clinic and community resources, education, and supports are needed to combat the sense of isolation and health care system challenges experienced by patients and their caregivers.
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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 | 0.095 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".