Metaphors of organizations in patient involvement programs: connections and contradictions
Bibliographic record
Abstract
PURPOSE: = 20) from participants in patient engagement activities from two case study organizations in Ontario, Canada. Inspired by classic organizational scholars, we ask "what is the organization that it might learn from patients?" DESIGN/METHODOLOGY/APPROACH: Patient involvement activities are used as part of quality improvement efforts in healthcare organizations worldwide. One fundamental assumption underpinning this activity is the notion that organizations must "learn from patients" in order to enact positive organizational change. Despite this emphasis on learning, there is a paucity of research that theorizes learning or connects concepts of learning to organizational change within the domain of patient involvement. FINDINGS: Through our analysis, we interpret a range of metaphors of the organization, including organizations as (1) power and politics, (2) systems and (3) narratives. Through these metaphors, we display a range of possibilities for interpreting how organizations might learn from patients and associated implications for organizational change. ORIGINALITY/VALUE: This analysis has implications for how the framing of the organization matters for concepts of learning in patient engagement activities and how misalignments might stymie engagement efforts. We argue that the concept and commitment to "learning from patients" would be enriched by further engagement with the sociology of knowledge and critical concepts from theories of organizational learning.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".