Teacher Induction Pathway of Physical and Health Education: A Case Study
Bibliographic record
Abstract
This article aims to describe teacher induction (TI) with reference to two physical and health education (PHE) teachers based on three variables: 1) self-efficacy, 2) motivation and 3) satisfaction. Our case study relies on individual telephone interviews conducted during each teacher’s first five years of teaching and allows for describing the perceptions of these two teachers who persevered in the profession. The findings reveal that self-efficacy relates above all to harmonious relations with students and teaching colleagues, an experienced teacher in particular, and evolves over the course of TI through a process of introspection undertaken by the new teacher. A surprising observation is that, despite the difficult conditions of substitute teaching during TI, teachers maintain that it offers diverse contexts and opportunities for learning about the profession; thus, substitute teaching appears to positively impact self-efficacy. The relatively high motivation fluctuates over time, based on whether it is approached positively in terms of professional relations, or negatively in terms of job insecurity. The same holds true for satisfaction, which alters over the years, either upwards if based on positive contact with students or downwards if based on difficult hiring conditions. During the induction process, satisfaction evolves towards the quest for academic freedom and an awareness of the teacher’s impact on his or her students. The conclusion explains how a strong feeling of self-efficacy can contribute to perseverance in the profession during the TI phase, even in the presence of difficult job conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".