Exploring social movement concepts and actions in a knowledge uptake and sustainability context: A concept analysis
Bibliographic record
Abstract
Objectives: To share a concept analysis of social movement aimed at advancing its application to evidence uptake and sustainability in health-care. Methods: We applied Walker and Avant method to clarify the concept of social movement in the context of knowledge uptake and sustainability. Peer-reviewed and grey literature databases were systematically searched for relevant reports that described how social movement action led to evidence-based practice changes in health and community settings. Titles, abstracts and full texts were reviewed independently and in duplicate, resulting in 38 included articles. Results: Social movement action for knowledge uptake and sustainability can be defined as individuals, groups, or organizations that, as voluntary and intrinsically motivated change agents, mobilize around a common cause to improve outcomes through knowledge uptake and sustainability. The 10 defining attributes, three antecedents and three consequences that we identified are dynamic and interrelated, often mutually reinforcing each other to fortify various aspects of the social movement. Examples of defining attributes include an urgent need for action, collective action and collective identity. The concept analysis resulted in the development of the Social Movement Action Framework. Conclusions: Social movement action can provide a lens through which we view implementation science. Collective action and collective identity - concepts less frequently canvassed in implementation science literature - can lend insight into grassroots approaches to uptake and sustainability. Findings can also inform providers and change leaders on the practicalities of harnessing social movement action for real-world change initiatives. By mobilizing individuals, groups, or organizations through social movement approaches, they can engage as powered change agents and teams that impact the individual, organizational and health systems levels to facilitate knowledge uptake and sustainability.
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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 | 0.065 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".