The impact of Codevelopment Action Learning on work self-efficacy, based on the results of a mixed-methods longitudinal study
Bibliographic record
Abstract
This article presents the key findings on participant development in Codevelopment Action Learning (CAL) groups from the second phase of Codev-Action, a Canadian action research partnership. The study used a mixed-methods design to quantitatively measure CAL’s contribution to work self-efficacy development in 154 participants from 50 CAL groups over a roughly one-year period. The study also used cross-sectional Qualitative Comparative Analysis (QCA) approach to identify which facilitation behaviour configurations were most likely to increase work self-efficacy among the participants who brought a topic to their group (n = 92). Quantitative results show a significant improvement in work self-efficacy, including perceived effectiveness with regard to teamwork, problem solving, and work politics. Qualitative analysis shows a set of five configurations involving 10 facilitation behaviours that, when used in CAL groups, can support increased work self-efficacy. These results provide empirical evidence for CAL’s contribution to the development of work self-efficacy. Given the well-known impact of self-efficacy on task performance, the progress made in CAL sessions is a significant asset for decision makers.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".