Mentor or evaluator? Assisting and assessing newcomers to the professions
Bibliographic record
Abstract
Purpose A four‐year longitudinal study was conducted on the school‐to‐work transition in four professions traditionally called “helping professions,” namely education, social work, occupational therapy, and physiotherapy. One goal of the study was to understand the nature and process of the mentoring relationships that develop and sustain newcomers in their professional life, especially the possible tension between the roles of mentor and evaluator. It was expected that this would help us in our jobs of preparing student practitioners and supporting their mentors and would offer models of practice suitable for other occupations. Design/methodology/approach Teams of researchers visited university students involved in professional training and engaged in a practicum during their final year at university. The students and their supervisors were interviewed separately according to a semi‐structured protocol, and each interview, lasting between 40 and 60 minutes, was audiotaped and transcribed. Findings An inherent contradiction between supervision‐as‐mentoring as the literature defines it and supervision‐as‐evaluation – as the universities and professions demand was observed. Evaluation as it is practiced often includes intangibles, even though the intended evaluation seems well‐defined. Practical implications Although this study was focused on practice in four particular professions, it is believed that the findings may be extrapolated to any enterprise where experienced practitioners are placed in a supervisory role with newcomers and are expected to perform the potentially conflicting role of mentor and evaluator. If the same person must mentor and evaluate the newcomer, they must be trained in these roles, evaluation guidelines must be clearly defined, and dialogue is of prime importance. Originality/value The paper focuses on the role of the universities in the preparation of new entrants to the “helping professions” and offers some suggestions for their support – which are generalizable to any workplace where beginners are trained, mentored and evaluated.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".