A Blended Electronic Illness Management and Recovery Program for People With Severe Mental Illness: Qualitative Process Evaluation Alongside a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: We conducted a trial to test the electronic Illness Management and Recovery (e-IMR) intervention to provide conclusions on the potential efficacy of eHealth for people with severe mental illness (SMI). In the e-IMR intervention, we used the standard IMR program content and methodology and combined face-to-face sessions with internet-based strategies on the constructed e-IMR internet platform. During the trial, the e-IMR platform was sparsely used. OBJECTIVE: This study aimed to evaluate the added value of the e-IMR intervention and the barriers and facilitators that can explain the low use of the e-IMR platform. METHODS: This process evaluation was designed alongside a multicenter, cluster randomized controlled trial. In this study, we included all available participants and trainers from the intervention arm of the trial. Baseline characteristics were used to compare users with nonusers. Qualitative data were gathered at the end of the semistructured interviews. Using theoretical thematic analyses, the data were analyzed deductively using a pre-existing coding frame. RESULTS: Out of 41 eligible participants and 14 trainers, 27 participants and 11 trainers were interviewed. Of the 27 participants, 10 were identified as users. eHealth components that had added value were the persuasive nature of the goal-tracking sheets, monitoring, and the peer testimonials, which had the potential to enhance group discussions and disclosure by participants. The low use of the e-IMR platform was influenced by the inflexibility of the platform, the lack of information technology (IT) resources, the group context, participants' low computer skills and disabilities, and the hesitant eHealth attitude of the trainers. CONCLUSIONS: The extent of eHealth readiness and correlations with vulnerabilities in persons with SMI need further investigation. This study shows that flexible options were needed for the use of e-IMR components and that options should be provided only in response to a participant's need. Use of the e-IMR intervention in the future is preconditioned by checking the available IT resources (such as tablets for participants) providing computer or internet guidance to participants outside the group sessions, evaluating the eHealth attitude and skills of trainers, and tailoring eHealth training to increase the skills of future e-IMR trainers. TRIAL REGISTRATION: Netherlands Trial Register NTR4772; https://www.trialregister.nl/trial/4621. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12913-016-1267-z.
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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 | 0.078 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".