Understanding everyday life and mental health recovery through CHIME
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how daily life reflects the recovery journeys of individuals with serious mental illness (SMI) living independently in the community. Design/methodology/approach The go-along technique, which blends participant observation and interviewing, was used to gather data from 19 individuals with SMI living in supported housing. Data were analyzed through the CHIME framework of personal recovery, which includes social connectedness, hope and optimism, identity, meaning in life, and empowerment. Findings Applying the CHIME framework to qualitative data reveals the multiple ways in which everyday experiences, within and beyond formal mental healthcare environments, shapes personal recovery processes. Research limitations/implications Combining novel methods and conceptual frameworks to lived experiences sharpens extant knowledge of the active and non-linear aspects to personal recovery. The role of the researcher must be critically considered when using go-along methods. Practical implications Practitioners working with this population should account for the role of socially supportive and financially accessible spaces and activities that support the daily work of recovery beyond the context of formal care and services. Originality/value This study utilizes an innovative method to illustrate the crucial role of daily and seemingly banal experiences in fostering or hindering personal recovery processes. It is also the one of the first studies to comprehensively apply the CHIME framework to qualitative data in order to understand the recovery journeys of individuals with SMI living in supported housing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.010 |
| 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".