The Importance of Mental Models in Implementation Science
Bibliographic record
Abstract
Implementation science is concerned with the study of adoption, implementation and maintenance of evidence-based interventions and use of implementation strategies to facilitate translation into practice. Ways to conceptualize and overcome challenges to implementing evidence-based practice may enhance the field of implementation science. The concept of mental models may be one way to view such challenges and to guide selection, use, and adaptation of implementation strategies to deliver evidence-based interventions. A mental model is an interrelated set of beliefs that shape how a person forms expectations for the future and understands the way the world works. Mental models can shape how an individual thinks about or understands how something or someone does, can, or should function in the world. Mental models may be sparse or detailed, may be shared among actors in implementation or not, and may be substantially tacit, that is, of limited accessibility to introspection. Actors' mental models can determine what information they are willing to accept and what changes they are willing to consider. We review the concepts of mental models and illustrate how they pertain to implementation of an example intervention, shared decision making. We then describe and illustrate potential methods for eliciting and analyzing mental models. Understanding the mental models of various actors in implementation can provide crucial information for understanding, anticipating, and overcoming implementation challenges. Successful implementation often requires changing actors' mental models or the way in which interventions or implementation strategies are presented or implemented. Accurate elicitation and understanding can guide strategies for doing so.
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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 | 0.127 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".