Addressing professional suitability in social work education: the experience and approach of field education coordinators
Bibliographic record
Abstract
The purpose of this research is to better understand the experience and approach of field education coordinators/directors in addressing student professional suitability in social work education so their insights can inform ongoing conversations within professional education programs on how to exercise „gatekeeping‟ responsibilities. The study begins with a critical reflection of my five year experience as a coordinator, which leads into a comprehensive review of the literature, followed by an analysis and discussion of information collected from a focus group with eight coordinators from across Canada, and an extensive web-based survey questionnaire administered to all current, and some former social work field education coordinators in Canada.\nIn brief, the results of this study reinforce the perception found in social work literature that gatekeeping predominantly falls to the field component of social work education. Field education coordinators report regularly encountering cases in which student‟ behaviours call into question their suitability for the profession. They perceive the field to hold the highest expectation of them to assess and address student professional suitability, followed by faculty, administration, the accreditation body, and students, and they assign a high level of importance to having an approach to addressing such concerns within their practice. They report employing a number of pre- and post-placement measures to addressing suitability concerns. However, current perceptions of gatekeeping as potentially oppressive and contrary to social work values creates tension in their experience that is exacerbated by workload pressures, and by the lack of clear criteria for determining suitability within school policies and accreditation standards. Respondents emphasized that more opportunities for dialogue between coordinators, faculty, administration, and field educators is needed. Also, although the majority reported relative satisfaction with their skills and knowledge, they suggested that further training and education would be beneficial, and strongly recommended that faculty, field, and administration participate in this education. Finally, a number of respondents also expressed the need for more support for their role and the field program in general within their school, and expressed concern for an apparent lack of institutional support for addressing professional suitability.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".