Implementing Internet-Based Self-Care Programs in Primary Care: Qualitative Analysis of Determinants of Practice for Patients and Providers
Bibliographic record
Abstract
BACKGROUND: Access to evidence-based interventions for common mental health conditions is limited due to geographic distance, scheduling, stigma, and provider availability. Internet-based self-care programs may mitigate these barriers. However, little is known about internet-based self-care program implementation in US health care systems. OBJECTIVE: The objective of this study was to identify determinants of practice for internet-based self-care program use in primary care by eliciting provider and administrator perspectives on internet-based self-care program implementation. METHODS: The objective was explored through qualitative analysis of semistructured interviews with primary care providers and administrators from the Veterans Health Administration. Participants were identified using a reputation-based snowball design. Interviews focused on identifying determinants of practice for the use of internet-based self-care programs at the point of care in Veterans Health Administration primary care. Qualitative analysis of transcripts was performed using thematic coding. RESULTS: A total of 20 physicians, psychologists, social workers, and nurses participated in interviews. Among this group, internet-based self-care program use was relatively low, but support for the platform was assessed as relatively high. Themes were organized into determinants active at patient and provider levels. Perceived patient-level determinants included literacy, age, internet access, patient expectations, internet-based self-care program fit with patient experiences, interest and motivation, and face-to-face human contact. Perceived provider-level determinants included familiarity with internet-based self-care programs, changes to traditional care delivery, face-to-face human contact, competing demands, and age. CONCLUSIONS: This exploration of perspectives on internet-based self-care program implementation among Veterans Health Administration providers and administrators revealed key determinants of practice, which can be used to develop comprehensive strategies for the implementation of internet-based self-care programs in primary care settings.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".