Picturing Recovery: A Photovoice Exploration of Recovery Dimensions Among People With Serious Mental Illness
Bibliographic record
Abstract
OBJECTIVES: Recovery from mental disorders encompasses multiple interrelated dimensions. This study used photovoice to explore how individuals with serious mental illness and a history of substance abuse and homelessness envisioned their recovery. A dimensional recovery model was applied to examine how the interrelationships between recovery dimensions supported consumers' recovery journeys. METHODS: Photovoice is a participatory research method that empowers people by giving them cameras to document their experiences and inform social action. Sixteen consumers recruited from two supported housing agencies participated in six weekly sessions to which they brought photographs that they took of persons and events in their lives that reflected recovery and wellness and discussed the meaning of the photographs in individual interviews and group sessions. The authors used pile-sorting, grounded theory, and a deductive template-analytic technique to analyze narrative and visual data. RESULTS: Spirituality, life achievements, and receiving and providing support were the most salient themes that emerged from the analysis and illustrate beneficial interrelationships between recovery dimensions. Participants discussed how they relied on their spirituality to support their sobriety and cope with addictions-aspects of clinical recovery. Educational and vocational achievements represented gains in functioning that contributed to increasing self-esteem and self-agency and reducing self-stigma. Social dimensions of recovery, such as receiving and giving support to loved ones, rippled through consumers' lives reducing isolation and enhancing their self-worth. CONCLUSIONS: The findings illustrate the value of participatory methods to understand what recovery signified to people with serious mental illness and how understanding the interrelationships between recovery dimensions can inform recovery-oriented services.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".