Vertical Versus Horizontal Assessment Methods for Scoring Physiotherapy Entrance Interviews
Bibliographic record
Abstract
Introduction: The purpose of this study was to provide insight into carryover bias in the vertical and horizontal methods of assessing virtual admission interviews for physiotherapy candidates and to estimate interrater reliability of items within the 2 assessment methods and assessors’ satisfaction with the new horizontal method of assessment. Methods: This was a quality improvement study using retrospective data analysis of 2 datasets. The vertical scoring method (2020 dataset) consisted of 2 assessors scoring all items for a single candidate. The horizontal method (2021 dataset) had assessors evaluate selected candidates for a single group of items. Assessors completed a virtual survey asking about their satisfaction with the new horizontal scoring method. To investigate carryover bias, multiquestion, multirater correlation matrices were generated for the 2020 and 2021 datasets. Interrater reliability was examined by calculating Shrout and Fleiss class 1 and 2 intraclass correlation coefficients (ICCs). Descriptive statistics were used to summarize scaling questions on the satisfaction survey. Open-ended questions were analyzed using content analysis to identify common themes. Results: Correlation matrices for the multiquestion, multirater correlation analysis supported carryover bias in the analysis of the 2020 dataset. In contrast for the 2021 data, higher correlations were obtained between raters within a question, demonstrating a reduced carryover effect. Interrater reliability based on the average of 2 raters was 0.62 (95% CI: 0.70–0.77) for the 2020 cohort and 0.74 (95% CI: 0.02–0.22) for the 2021 cohort. The ICC difference between the datasets was statistically significant (Z = 2.40, P = .016). Most assessors agreed that they enjoyed reviewing applicants more horizontally than vertically. Discussion and Conclusions: Results of this study demonstrated reduced carryover bias and increased interrater reliability and assessor satisfaction with the horizontal method of scoring physiotherapy admissions interviews compared with the traditional vertical method. Continued exploration of admissions processes is vital to ensure the fairest method of conducting online physiotherapy admission interviews for a large pool of candidates.
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 | 0.135 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".