Consumer and provider responses to a computerized version of the Illness Management and Recovery Program.
Bibliographic record
Abstract
OBJECTIVE: To explore mental health consumer and provider responses to a computerized version of the Illness Management and Recovery (IMR) program. METHOD: Semistructured interviews were conducted to gather data from 6 providers and 12 consumers who participated in a computerized prototype of the IMR program. An inductive-consensus-based approach was used to analyze the interview responses. RESULTS: Qualitative analysis revealed consumers perceived various personal benefits and ease of use afforded by the new technology platform. Consumers also highly valued provider assistance and offered several suggestions to improve the program. The largest perceived barriers to future implementation were lack of computer skills and access to computers. Similarly, IMR providers commented on its ease and convenience, and the reduction of time intensive material preparation. Providers also expressed that the use of technology creates more options for the consumer to access treatment. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: The technology was acceptable, easy to use, and well-liked by consumers and providers. Clinician assistance with technology was viewed as helpful to get clients started with the program, as lack of computer skills and access to computers was a concern. Access to materials between sessions appears to be desired; however, given perceived barriers of computer skills and computer access, additional supports may be needed for consumers to achieve full benefits of a computerized version of IMR.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".