Using Realistic Evaluation to understand how interventions work on dairy farms
Bibliographic record
Abstract
Interventions that aim to help farmers change on-farm practices recommend that advisors communicate effectively with farmers, work collaboratively to set goals and provide farmers with resources that are applicable to the farm context. We developed an intervention that aimed to help farmers modify and use a standard operating procedure (SOP) for colostrum management; failure of passive transfer of immunoglobulins is common on dairy farms and SOPs for colostrum management are increasingly required by farm animal welfare assurance programs. We used Realistic Evaluation to evaluate whether, how and why our intervention to help farmers modify and use SOPs for colostrum management facilitated change and provide recommendations based on our approach that can improve the design and implementation of future interventions. We used a multiple case study on five farms over 8 months, collecting data through interviews, participant observation, document analysis and field notes. We identified three mechanisms that influenced whether participants modified and used their SOP. The purpose mechanism distinguished between participants who thought the aim of the SOP was for farm staff to learn and understand how to complete a task versus those who thought that the SOP was only useful for compliance with assurance programs. The utility mechanism distinguished between participants who thought that the SOP would be helpful for daily use on their farm, versus those who did not. The physical text mechanism distinguished between participants who used the templates we provided to modify and use their SOP, versus those who did not. A key contextual factor on all farms was participant belief of having capable and engaged staff on their farm; modification and use of the SOP did not occur unless this was the case. To facilitate change, intervention developers should actively participate in the intervention to develop an understanding of farmer needs, understand the purpose behind different goals set by farmers and integrate tools, advice and resource demonstrations when possible. We conclude that Realistic Evaluation is a useful framework for evaluating how contexts and mechanisms generate outcomes on farms, and to understand how, and in which contexts, complex interventions facilitate change. We suggest that this approach can improve the success of interventions and help direct the approaches used on different farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".