The Healthy Learning Organizations Model: Lessons Learned From the Canadian Federal Public Service
Bibliographic record
Abstract
This study evaluates the predictive validity of the Healthy Learning Organizations (HLO) model in explaining mental health and organizational commitment among executives from the public sector. Data were derived from a cross-sectional sample of executives from the Canadian federal public service ( N = 1,601). Latent class analyses (LCA) assessed whether (a) associative patterns in executives’ psychosocial work environment and organizational learning process expressed a typology of healthy and learning organizations; and (b) executives’ mental health and organizational commitment varied according to this typology. LCA yielded a three-latent class solution, supporting evidence of (a) differential arrangements in the healthy and learning components of the HLO model; and (b) differential impacts on executives’ psychological distress and organizational commitment (i.e., affective, continuance). The HLO model offers novel grounds to assess healthy and learning organizations in the public administration sector.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".