Collaborative action around implementation in Collaborations for Leadership in Applied Health Research and Care: towards a programme theory
Bibliographic record
Abstract
OBJECTIVES: In theory, greater interaction between researchers and practitioners should result in increased potential for implementation. However, we know little about whether this is the case, or what mechanisms might operate to make it happen. This paper reports findings from a study that is identifying and tracking implementation mechanisms, processes, influences and impacts in real time, over time in the Collaborations for Leadership in Applied Health Research and Care (CLAHRCs). METHODS: This is a longitudinal, realist evaluation case study. The development of the conceptual framework and initial hypotheses involved literature reviewing and stakeholder consultation. Primary data were collected through interviews, observations and documents within three CLAHRCs, and analysed thematically against the framework and hypotheses. RESULTS: The first round of data collection shows that the mechanisms of collaborative action, relationship building, engagement, motivation, knowledge exchange and learning are important to the processes and outcomes of CLAHRCs' activity, including their capacity for implementation. These mechanisms operated in different contexts such as competing agendas, availability of resources and the CLAHRCs' brand. Contexts and mechanisms result in different impact, including the CLAHRCs' approach to implementation, quality of collaboration, commitment and ownership, and degree of sharing and managing knowledge. CONCLUSION: Emerging features of a middle range theory of implementation within collaboration include alignment in organizational structures and cognitive processes, history of partnerships, responsiveness and resilience in rapidly changing contexts. CLARHCs' potential to mobilize knowledge may be further realized by how they develop insights into their function as collaborative entities.
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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 | 0.084 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".