Teacher Development: A Patchwork-Text Approach to Enhancing Critical Reflection in Veterinary and Para-Veterinary Educators
Bibliographic record
Abstract
Reflection is an essential component of teacher-development programs, and reliable, valid methods to teach, assess, and evaluate reflection are critical. However, it is important that appropriate methods are created for and evaluated across multiple disciplinary backgrounds, as the participants' backgrounds are a major factor in the development of critical reflection. The patchwork-text approach is a narrative process that is predominantly focused on the personal development of the individual. The current study used the patchwork-text approach for the development of reflection in participants with a science background who had not used a reflective approach for personal development before. Twenty summative essays and 103 formative essays from 21 participants who underwent a 1-year higher-education teacher-development program were analyzed to assess whether the quality and quantity of reflective writing was enhanced through a regular, iterative process of reflective writing with feedback. The analysis of the essays involved the use of a predefined set of criteria for identifying the different reflective levels from 1 to 4 and the calculation of a reflective score to evaluate the overall development. The results show a clear improvement of higher-level critical thinking as the participants progressed through their course. Higher levels of reflection were achieved particularly where a unit focused on a familiar area for the participant as opposed to one in which the participant had less experience. The analysis provides evidence that the patchwork text is a useful method for development and evaluation of reflection in participants with a veterinary/animal-science base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".