How trainees develop an initial theory of practice: A process model of tentative identifications
Bibliographic record
Abstract
Abstract Aim: The increasing importance of integrative practice highlights the need to explore how trainees develop their theoretical stance. This investigation explored the experiences of trainees to elaborate a model of how they developed their personal theories of practice. Method: Seventeen Masters level trainee counsellors kept weekly journals recording how they developed a working theory of practice. Grounded theory analysis of the journals was used to develop a model of the process. Findings: The resulting Process Model of Tentative Identifications illustrates how a personal theory developed through trainees' tentative identifications with theories of practice, and how factors such as reading, personal philosophy, practice, and supervision interact to produce the identifications. A diagram of the model highlights the relationships among a variety of personal and professional factors that ranged from highly abstract to concrete and practice‐based. Discussion: The model is consistent with several factors identified in previous research and highlights how trainees develop working theories of practice.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".