Implementing & evaluating a community of practice for health visiting - final report
Bibliographic record
Abstract
This report presents the context, approach and findings arising from implementing and evaluating a Community of Practice (CoP) for Health Visitors across Kent and Medway funded by Health Education England (Kent, Surrey and Sussex) and built on the launch of the Communities of Practice concept in Kent and Medway (Keen et al 2013). The intention of the CoP, with its focus on health visiting practice, was to support a Health Visitor from each locality across Kent and Medway to: \n•develop their skills in practice development and clinical leadership \n•create effective learning cultures within which students and practitioners can flourish \n•explore how the effectiveness of health visiting can be demonstrated \n \nTwo interrelated methodologies, action research and practice development were selected because they both focus on practical action in the workplace that is systematically implemented and evaluated through collaborative, inclusive and participative approaches. Three overarching processes (methods) were used to support the health visitor clinical leaders included active learning (Dewing 2008), action learning (McGill & Beaty 2001) and critical companionship - a helping relationship that focuses on helping a practitioner to learn (Titchen 2000). \n \nWithin the lifetime of the project 18 co-researchers across two cohorts were recruited to the action learning sets; recruitment of co-researchers was undertaken in partnership with service managers. Four powerful influences emerged that impacted on the co-researchers’ participation in the project and also the potential for sustainability of the CoP project. \n \nUnderstanding the factors and strategies that influence the successful implementation of Communities of Practice is important to others who may be involved in similar initiatives. The limitations of the project are explored with recommendations for a range of organisations. Resources and outputs are shared and promoted widely to help others develop effective workplace cultures that use the workplace as the main resource for 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 | 0.093 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".