Canadian Senior Student Affairs Officers’ Perspectives and Experiences Working in Higher Education
Bibliographic record
Abstract
The changing higher education landscape requires Senior Student Affairs Officers (SSAOs) to have dynamic and diverse competencies and skills to lead this complex work; however, there is limited research examining perceptions of skills needed for Canadian SSAOs. This article identifies skills and competencies required for this leadership role as well as issues and trends facing the field of Student Affairs and Services in Canada. Drawing on data from 75 respondents to the 2022 SSAO Survey representing Canadian universities and colleges, findings show that the top competencies required of an SSAO are diversity, communication, and student development. SSAOs also identified the signals, trends, and drivers facing current and future SSAOs in the field, including responding to students and academics, online learning and services, and mental health and well-being. Results also showed the types of professional development most valued by SSAOs were in-person workshops and mentoring relationships. A unique contribution of this research was gathering SSAOs’ assessments of their competencies related to Indigenization, decolonization, and reconciliation. Participants reported confidence regarding doing land acknowledgements, supporting Indigenous students in finding resources, being open to Indigenous ways of knowing, and expanding their own understanding. They also identified the need for more training to develop relationships with community, human resources strategies for recruitment and retention of Indigenous staff, and fiscal plans and policies for Indigenous student services. The study also considers the implications of leadership training and development for the next generation of SSAO leaders. This study highlights the need for ongoing research into SSAO competencies and the professionalization of the field, as well as the role of research in making evidence-based decisions within Student Affairs and Services.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".