Absorptive Capacity and the Uptake of New Discharge Practices in Ontario Hospitals
Bibliographic record
Abstract
There is a growing realization that there is a relationship between how patients are discharged from hospital and their subsequent readmission, however little research has examined how hospitals discharge patients. “Health literate discharge practices” have been associated with improved patient and health system outcomes. The goals of this study were to examine how Ontario hospitals adopt and use health literate discharge practices, and it asked: what are the absorptive capacity metaroutines that hospitals use in the uptake of health literate discharge practices? Key informant interviews were conducted with 20 participants from 10 hospital sites across Ontario. Our thematic analysis led to the identification of eight metaroutines: allocating resources, building and nurturing external relationships, fostering internal networks, standardizing processes, responding to environmental mandates, engaging patients and families, fostering participative decision making and scanning the external environment. Several barriers to the use of these metaroutines were identified including poor communication and lack of standardized processes. We offer a series of propositions, based on our findings and extant research on organizational learning, to form a new conceptual model regarding the adoption and use of health literate discharge practices in hospitals. The results of this qualitative study offer insights into the metaroutines that hospital managers and leaders use to support the uptake of health literate discharge practices. The findings of this study could be extended to the adoption and use of other evidence-based practices to improve patient care and outcomes.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".