The Learning History Methodology: An Infrastructure for Collective Reflection to Support Organizational Change and Learning
Bibliographic record
Abstract
Organization members often complain about insufficient time to reflect collectively as they grapple with constant significant changes. The Learning History methodology can support this collective reflection. Given the scant empirical studies of this action research approach, the present paper fills this gap by giving an overview of this methodology and by presenting a qualitative study that answers the following research question: How does the Learning History methodology contribute to collective reflection among organization members during major organizational change? To answer this question, an empirical research project was led within five healthcare organizations in Canada during their implementation of the Planetree person-centered approach to management, care, and services. The data set includes 150 semi-structured interviews, 20 focus groups and 10 feedback meetings involving organization members representing all hierarchical levels in the five participating institutions. The results highlight the five types of contributions of the Learning History methodology to collective reflection within the five institutions that participated in the study: 1) a process of expression, dialogue, and reflection among organization members; 2) a portrait of the change underway; 3) a support tool for the change process; 4) a vector for mobilizing stakeholders; and 5) a source of organizational learning. The results also show how organization members’ collective reflection is built through the various stages of the Learning History methodology. By demonstrating that this collective reflection leads to true organizational learning, the findings position the Learning History as a research-action method useful both from a research standpoint and as an organizational development tool. In the conclusion, lessons learned using the LH approach are shared from a researcher’s perspective. This paper should interest researchers and practitioners who seek research methodologies that can offer an infrastructure for collective reflection to support organizational change and learning.
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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 | 0.063 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".