Bibliographic record
Abstract
An emerging problem with graduate education is the unprecedented rise in mental health and wellbeing concerns across higher education institutions in Canada. Graduate education is widely associated with emotional, physical, and psychological stress. Graduate students are at risk of the onset of mental health illnesses due to a culture of acceptance that graduate studies is synonymous with stress and anxiety. This Organizational Improvement Plan (OIP) explores approaches to improve the mental health and wellness of Science, Technology, Engineering, and Math (STEM) graduate students to promote their personal wellbeing and academic success. The goal of my Problem of Practice (PoP) is to increase awareness of the complex factors and address the systemic barriers that contribute to STEM graduate student mental health illnesses at University Z, and to develop strategies to mitigate their onset. Transformational and distributed leadership practices underpinned by a social justice lens are the chosen leadership approaches. Nadler and Tushman’s Congruence Model (1980) is used as a thought map to conduct a comprehensive organizational analysis which includes a partial PESTE analysis. Kotter’s Eight-Stage Model (1996) is integrated with the Change Path Model (2016) to create a hybrid CDI x K Model to lead the change process. A resulting policy-based solution to empower STEM graduate students is pursued through the OIP. A thorough implementation plan that details objectives, actions, personnel, and timelines is presented. The plan is monitored and evaluated through the application of Deming’s (1993) PDSA cycle. The OIP presents next steps, future considerations, and a reflective conclusion.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".