IMPACT OF THE BE EPIC: A PERSON-CENTERED COMMUNICATION INTERVENTION FOR HOME CARE WORKERS
Bibliographic record
Abstract
Abstract The current study assessed the impact of Be EPIC, an innovative, evidence-informed and theoretically-grounded 6-week person-centered communication intervention for personal support workers (PSWs) caring for persons with dementia. Be EPIC focuses on [E]nvironment contexts for using [P]erson-centered communication, while considering client relationships ([I] matter too), and [C]lients’ abilities, life history and preferences during routine care. A pre- post-Be EPIC comparative design included an intervention (n=13) and a 6-week waitlist control group (n=10) who completed the same communication-related questionnaire. A Two-Way Mixed ANOVA showed a significant group by time interaction for perceived communication skill (F(1, 21) = 4.67, p = .042, ηp2= .18). Simple main effects analysis showed that participants who completed Be EPIC reported feeling more confident in communicating with persons with dementia (Mpre = 13.46; SD = .76; Mpost = 16.31, SD = .85). There was no significant change in the control group. Similarly, there was a significant group by time interaction for perceived helpfulness of communication strategies (F(1, 21) = 6.23, p = .021, ηp2 = .23). Simple main effects analysis showed that participants who completed Be EPIC reported significant increases in the helpfulness of effective communication strategies (Mpre = 36.92; SD = 3.42; Mpost = 43.15, SD = 3.21), with no significant change among controls. Findings indicate that Be EPIC enhanced PSWs’ confidence in communicating with persons with dementia and enhanced their perception of the helpfulness of effective communication strategies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".