Potential Implementers’ Perspectives on the Development and Implementation of an e–Mental Health Intervention for Caregivers of Adults With Chronic Kidney Disease: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: e-Mental health interventions can improve access to mental health support for caregivers of people living with chronic kidney disease (CKD). However, implementation challenges often prevent effective interventions from being put into practice. To develop an e-mental health intervention for caregivers of people living with CKD that is optimized for future implementation, it is important to engage professionals that may endorse or deliver the intervention (ie, potential implementers) during intervention development. OBJECTIVE: This study aims to explore the perspectives of potential implementers working in kidney care, in mental health care, or at nonprofit organizations regarding the design and implementation of an e-mental health intervention for caregivers of people living with CKD. METHODS: Potential implementers (N=18) were recruited via National Health Service Trusts, email, and social media advertisements to participate in semistructured video interviews. Interview questions were informed by the Consolidated Framework for Implementation Research (CFIR). Data were analyzed using a deductive analysis approach using the CFIR, with inductive coding applied to relevant data not captured by the framework. RESULTS: A total of 29 generic categories, related to 17 CFIR constructs, were identified. The perceived fit between the intervention and implementation context (ie, existing service delivery models and work routines) and existing social networks among potential implementers were perceived as important factors in enhancing implementation potential. However, a need for capacity building among potential implementers to create systems to support the identification and referral of caregivers to an e-mental health intervention was identified. Equity concerns were raised regarding the intervention, highlighting the importance of incorporating an equity lens during intervention design to enhance accessibility and adoption. CONCLUSIONS: Potential implementers provided valuable insights into key design and implementation factors to help inform the development of an e-mental health intervention for caregivers of people living with CKD. Incorporating their feedback can help ensure the intervention is acceptable and inform the selection of future implementation strategies to enhance the implementation potential of the intervention. Potential implementers should continue to be engaged throughout intervention development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".