A mixed method mentorship audit: assessing the culture that impacts teaching and learning in a polytechnic
Bibliographic record
Abstract
Abstract The benefits of mentorship to individuals in post-secondary relate to wellbeing, satisfaction, and perceived success which translates to organizational commitment. Mentorship improves skills in academic roles and leadership, yet a disconnect remains on what mentees and mentors expect and what institutions provide. Supports are required for mentorship to be effective in empowering employees and creating a culture that espouses competence and autonomy through collaboration and creativity. The aim of this research was to replicate and advance an earlier study assessing nursing and health sciences in a polytechnic to describe the perceived mentorship culture for faculty, professional services, and leadership, across a provincial organization. This was accomplished through a sequential descriptive mixed methods study assessing the building blocks and hallmarks of a Mentorship Culture Audit. This paper reports on both the comparative assessment from 2013 and this new quantitative survey, along with a qualitative component enhancing the understanding of the mentorship culture within a polytechnic providing a variety of programming for vocational students. The audit revealed the employee perception of a mentorship culture to a mean of 4.52 on a seven-point Likert scale and noted areas of strength or infrastructure to be developed. Qualitative data portrayed further understanding where hallmarks of mentorship promoted or were lacking for informal or formal structures. Organizations benefit from mentorship. Tailoring mentorship to a framework ensures mentorship is anchored for success. This study is unique in its replication, the mixed methods approach, and its originality as an organizational level mentorship assessment.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".