Assessing the Time for Living and Caring (TLC) Study: Mixed-Methods Feasibility Study of a Web-Based Caregiver Intervention to Improve Respite
Bibliographic record
Abstract
Background: Interventions that are self-administered and delivered online are increasingly being seen as a flexible way to support family caregivers. Intervention research should prioritize the measurement of feasibility throughout all of the stages of intervention development and evaluation to provide the essential feedback loop needed for the iterative development and refinement process. Objective: We describe the methodology and data used to assess the feasibility, usability, and acceptability of the Time for Living and Caring (TLC) intervention, a technology-delivered intervention (app) for dementia caregivers to improve respite time use. Methods: The feasibility analysis is theoretically guided by a multidimensional definition of feasibility and uses a mixed-methods research design. Stakeholder feedback collected via focus groups during intervention development (n=15), self-reported surveys from participants enrolled in the pilot trial of the intervention (n=163), surveys of a nationwide sample of respite providers (n=57), and end-user statistics, captured passively by Google Analytics from those using the app, were used in the feasibility analysis of the TLC intervention. Results: The TLC study used an appropriate design and data collection procedures, along with acceptable recruitment capability. Out of 5 intervention features, 4 received favorable ratings (range of 82%-99%) by intervention participants and respite providers, which, when combined with open-ended recommendations for improvements, indicate a high degree of usability. Acceptability was measured through appraisal of the intervention experience (135/159, 85% positive), potential future use (127/163, 78%), willingness to recommend (148/163, 91%), and perceived benefit (135/163, 83%). Conclusions: Taken together, the data suggest that the TLC app is a promising intervention that could be implemented as an on-demand resource for respite-using caregivers, irrespective of where they are located or when they choose to access it. Additionally, this paper provides a blueprint for systematically evaluating multiple dimensions of feasibility, using various forms of mixed-methods data collected during intervention development and pilot testing of an intervention, which should help streamline the eventual implementation of effective interventions in real-world settings.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".