Learning to Be Supervisors: A Qualitative Investigation of Difficulties Experienced by Supervisors-in-Training
Bibliographic record
Abstract
This study examined the challenges and difficulties of supervisors-in-training during the course of providing individual and group supervision to master's-level counseling trainees using both group and individual formats. We interviewed 10 supervisors-in-training regarding their supervisory experiences with master's-level counselor trainees. Data analysis used a variation of the consensual qualitative research method (Hill, Thompson, & Nutt-Williams, Citation1997). The results included five categories of difficulties: (1) managing the “gatekeeping” role, (2) simultaneously managing multiple processes, (3) experiencing an ongoing attempt at establishing a supervisory stance, (4) self-doubt about their abilities as supervisors, and (5) managing dynamics with their co-supervisors. We discuss some reasons for the training difficulties that the doctoral supervisors-in-training experienced in assuming a new role and offer implications for supervision curricula and training in doctoral programs.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".