Reimagining Community Relationships for Organizational Learning: a Scoping Review With Implications for a Learning Health System
Bibliographic record
Abstract
Abstract Background: Communities represent a highly relevant source of knowledge with regard to not only healthcare performance but also sociocultural context, yet their role in learning health systems has not been studied. Situating the learning health system as an organization, this paper explores the phenomenon of organizational learning from or with communities (defined as one of ‘the people,’ such as a town, a specific patient group or another group directly receiving a healthcare service).Methods: We conducted a scoping review to determine what is known about organizational learning from or with communities that the organization serves, and to contribute to a more comprehensive evidence base for building and operating learning health systems. In March 2019, we systematically searched six academic databases and grey literature, applying no date limits, for English language materials that described organizational learning in relation to knowledge transfer between an organization and a community. Numerous variables were charted in Excel and synthesized using frequencies and thematic analysis. We updated this search in August 2020. Results: In total, 42 documents were included in our analysis. We found a disproportionate emphasis on learning explicit knowledge from community rather than on tacit knowledge or learning in equal partnership with community. Our review also revealed inconsistently defined concepts, tenuously linked with their theoretical and empirical foundations. Our findings provide insight to understand the organization-community learning relationship, including motives and power differentials; types of knowledge to be learned; structures and processes for learning; and transformative learning outcomes.Conclusions: Our review makes a singular contribution to organizational learning literatures by drawing from diverse research disciplines such as health services, business and education to map what is known about learning from or with community. Broadly speaking, learning health systems literature would benefit from additional research and theory-building within a sociological paradigm so as to establish key concepts and associations to understand the nature of learning with community, as well as the practices that make it happen.
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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 | 0.044 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".