Spreading and sustaining best practices for home care of older adults: a grounded theory study
Bibliographic record
Abstract
BACKGROUND: Improving health care quality requires effective and timely spread of innovations that support evidence-based practices. However, there is limited rigorous research on the process of spread, factors influencing spread, and models of spread. It is particularly important to study spread within the home care sector given the aging of the population, expansion of home care services internationally, the high proportion of older adult users of home care services, and the vulnerability of this group who are frail and live with multiple chronic conditions. The purpose of this study was to understand how best practices related to older adults are spread within home care organizations. METHODS: Four home care organizations in Ontario, Canada that had implemented best practices related to older adults (falls prevention, pain management, management of venous leg ulcers) participated. Using a qualitative grounded theory design, interviews were conducted with frontline providers, managers, and directors at baseline (n = 44) and 1 year later (n = 40). Open, axial, and selective coding and constant comparison analysis were used. RESULTS: A model of the process of spread of best practices within home care organizations was developed. The phases of spread included (1) committing to change, (2) implementing on a small scale, (3) adapting locally, (4) spreading internally to multiple users and sites, and (5) disseminating externally. Factors that facilitated progression through these phases were (1) leading with passion and commitment, (2) sustaining strategies, and (3) seeing the benefits. Project leads, champions, managers, and steering committees played vital roles in leading the spread process. Strategies such as educating/coaching and evaluating and feedback were key to sustaining the change. Spread occurred within the home care context of high staff and manager turnover and time and resource constraints. CONCLUSIONS: Spread of best practices is optimized through the application of the phases of spread, allocation of resources to support spread, and implementing strategies for ongoing sustainability that address potential barriers. Further research will help to understand how best practices are spread externally to other organizations.
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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 | 0.050 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".