Action‐project method: An approach to describing and studying goal‐oriented joint actions
Bibliographic record
Abstract
CONTEXT: Practicing health professionals and educators frequently act together in an interdependent or joint capacity to reach goals. Teaching or learning a new skill or engaging with patients in shared decision-making exemplifies this joint and goal-directed nature of Health Professions Education (HPE) and practice. However, building a robust understanding of the complexity of action, and joint action in particular, in HPE or patient care remains a challenge because of a limited number of methodologies available within HPE research. METHODS: In this manuscript, we describe the Action-Project Method (A-PM) as a qualitative research approach that can be used to describe and understand goal-directed joint actions. A-PM is grounded in contextual action theory and is a methodology focussed on action as an object of study, as it is occurring. A-PM uses three distinct perspectives to understand goal-directed joint actions: observable behaviour, internal processes (i.e. reported thoughts and feelings) and the social meaning reflected in goals. Data collection in A-PM involves observations, interviews, recording of actions and a self-confrontation procedure-where participants watch video-recorded segments of action and reflect on their internal processes, describing what they were thinking or feeling as they were completing the action. Together, the rich data generated and the layered approach to analysis provide a means to better understand the joint actions embedded in complex systems and collaborative work. Furthermore, the participants are treated as equal partners within A-PM, ensuring data equity even when the research context includes hierarchical relationships. DISCUSSION: Given increasing recognition to the importance of teamwork, relationships, interdependence, complex environments and centring patient or learner voices, A-PM is a valuable research approach for HPE. A-PM deepens our research arsenal with an approach that focusses on interdependent dyads or teams and provides a deeper understanding for how individuals engage together in goal-oriented actions.
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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 | 0.048 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".