Bibliographic record
Abstract
PURPOSE: Hospital leaders are being challenged to become more consumer-oriented, more interprofessional in their approach to care and more focused on outcome measures and continuous quality improvement. The concept of the learning organization could provide the conceptual framework necessary for understanding and addressing these various challenges in a systematic way. The paper aims to discuss these issues. DESIGN/METHODOLOGY/APPROACH: A scan of the literature reveals that this concept has been applied to hospitals and other health care institutions, but it is not known to what extent this concept has been linked to hospitals and with what outcomes. To bridge this gap, the question of whether learning organizations are the answer to improving hospital care needs to be considered. Hospitals are knowledge-intensive organizations in that there is a need for constant updating of the best available evidence and the latest medical techniques. It is widely acknowledged that learning may become the only sustainable competitive advantage for organizations, including hospitals. FINDINGS: With the increased demand for accountability for quality care, fiscal responsibility and positive patient outcomes, exploring hospitals as learning organizations is timely and highly relevant to senior hospital administrators responsible for integrating best practices, interprofessional care and quality improvement as a primary means of achieving these outcomes. ORIGINALITY/VALUE: To date, there is a dearth of research on hospitals as learning organizations as it relates to improving hospital care.
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.
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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".