The Contingencies of Organizational Learning in Long-Term Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We apply the theoretical frameworks of knowledge transfer and organizational learning, and findings from studies of clinical practice guideline (CPG) implementation in health care, to develop a contingency model of innovation adoption in long-term care (LTC) facilities. Our focus is on a particular type of innovation, CPGs designed to improve the quality of LTC. Our interest in this area is founded on the premise that the ability of LTC organizations to adopt and sustain the use of innovations like CPGs is contingent on the initial capacity these institutions have to learn about them, and on the presence of factors that contribute to capacity building at each stage of innovation adoption. Based on our review of relevant theory, we develop a set of fifteen testable propositions that relate factors operating at the guideline, individual, organizational, and environmental levels in LTC institutions to stages of guideline adoption/transfer. Our model offers insights into the complexities of adopting and sustaining innovations in LTC facilities particularly, in health care organizations specifically, and in service organizations generally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it