FEASIBILITY OF BE EPIC: A DEMENTIA-FOCUSED PERSON-CENTERED COMMUNICATION INTERVENTION FOR HOME CARE WORKERS
Bibliographic record
Abstract
This study assessed the feasibility of Be EPIC, an innovative, evidence-informed and theoretically-grounded person-centered communication intervention for personal support workers (PSWs) caring for clients with dementia. Be EPIC focuses on assessing the [E]nvironment, using [P]erson-centered communication, focusing on client relationships ([I] matter too), and incorporating the [C]lient’s abilities, life history and preferences during routine care. Informed by social cognitive theory, Be EPIC’s delivery includes: self-monitoring, supervised practice with simulated patients and adult-day center clients with dementia, and feedback/support from peers, simulated patients, and trainers. Feasibility was assessed post-training, using semi-structured interviews with PSWs (N=11). This study used a phenomenological approach to assess: acceptability, implementation, practicability, demand, and preliminary efficacy. Acceptability was reflected by the theme, helpful and relevant training. Themes supporting implementation included interactive training, realistic simulations, and exposure to diverse clients and expert staff at the adult-day center. Practicability was evidenced by the theme, support to attend training, and included facilitators and barriers. Facilitators were employers, family, and research staff. Barriers were employer-related. Demand for Be EPIC revealed two themes: participants’ personal motivation and barriers to applying newly learned knowledge and skills. Barriers were at employer and government levels. Support for preliminary efficacy was evidenced by the theme, applying newly learned knowledge and skills with home care clients. Four subthemes emerged that mapped directly onto the foci of Be EPIC. The findings support the feasibility of Be EPIC and highlight structural barriers from employers and government that must be addressed to ensure that clients with dementia receive person-centered care.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".