Impact of Jointly Using an e–Mental Health Resource (Self-Management And Recovery Technology) on Interactions Between Service Users Experiencing Severe Mental Illness and Community Mental Health Workers: Grounded Theory Study
Bibliographic record
Abstract
BACKGROUND: e-Mental health resources are increasingly available for people who experience severe mental illness, including those who are users of community mental health services. However, the potential for service users (SUs) living with severe mental illness to use e-mental health resources together with their community mental health workers (MHWs) has received little attention. OBJECTIVE: This study aims to identify how jointly using an interactive website called Self-Management And Recovery Technology (SMART) in a community mental health context influenced therapeutic processes and interactions between SUs and MHWs from their perspective. METHODS: We conducted a qualitative study using a constructivist grounded theory methodology. Data were collected through individual semistructured interviews with 37 SUs and 15 MHWs who used the SMART website together for 2 to 6 months. Data analysis involved iterative phases of coding, constant comparison, memo writing, theoretical sampling, and consultation with stakeholders to support the study's credibility. RESULTS: A substantive grounded theory, discovering ways to keep life on track, was developed, which portrays a shared discovery process arising from the SU-worker-SMART website interactions. The discovery process included choosing to use the website, revealing SUs' experiences, exploring these experiences, and gaining new perspectives on how SUs did and could keep their lives on track. SUs and MHWs perceived that their three-way interactions were enjoyable, beneficial, and recovery focused when using the website together. They experienced the shared discovery process as relationship building-their interactions when using the website together were more engaging and equal. CONCLUSIONS: Jointly using an e-mental health resource elicited recovery-oriented interactions and processes between SUs and MHWs that strengthened their therapeutic relationship in real-world community mental health services. Further work to develop and integrate this novel use of e-mental health in community mental health practice is warranted.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".