Internet-Based Psychotherapy Intervention for Depression Among Older Adults Receiving Home Care: Qualitative Study of Participants’ Experiences
Bibliographic record
Abstract
BACKGROUND: Depression is common among homebound older adults. Internet-based cognitive behavioral therapy (iCBT) is a promising but understudied approach for treating depression among older adults with disabilities. OBJECTIVE: This study aims to understand the experiences of homebound older adults who participated in a pilot feasibility trial of an iCBT for depression. METHODS: The participants included 21 homebound older adults who participated in a generic iCBT program that was not specifically designed for older adults and 8 home care workers who assisted in the iCBT program. Informants completed semistructured individual interviews, which were transcribed verbatim and analyzed using methods informed by grounded theory. A hierarchical code structure of themes and subthemes was developed after an iterative process of constant comparisons and questionings of the initial codes. The data analysis was conducted by using dedoose, a web app for mixed methods research. RESULTS: Three themes and various subthemes emerged related to participants' experience of the iCBT intervention, as follows: intervention impact, which involved subthemes related to participants' perceived impact of the intervention; challenges and difficulties, which involved subthemes on the challenges and difficulties that participants experienced in the intervention; and facilitators, which involved subthemes on the factors that facilitated intervention use and engagement. CONCLUSIONS: iCBT is a promising intervention for homebound older adults experiencing depression. Home care workers reported improved relationships with their clients and that the program did not add a burden to their duties. Future programs should involve accessible technical features and age-adapted content to improve user experience, uptake, and adherence. TRIAL REGISTRATION: ClinicalTrials.gov NCT04267289; https://clinicaltrials.gov/ct2/show/NCT04267289.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 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".