Bibliographic record
Abstract
Although I find Graham Lowe and Ben Chan's logic model and work environment metrics thought provoking, a healthy work environment framework must be more comprehensive and consider the addition of recommended diagnostic tools, vehicles to deliver the necessary change and a sustainability strategy that allows for the tweaking and refinement of ideas. Basic structure is required to frame and initiate an effective process, while allowing creativity and enhancements to be made by organizations as they learn. I support the construction of a suggested Canadian health sector framework for measuring the health of an organization, but I feel that organizations need to have some freedom in that design and the ability to incorporate their own indicators within the established proven drivers. Reflecting on my organization's experience with large-scale transformation efforts, I find that emotional intelligence along with formal leadership development and front-line engagement in Lean process improvement activities are essential for creating healthy work environments that produce the balanced set of outcomes listed in my hospital's Balanced Scorecard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".