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Record W4294012667 · doi:10.1097/jte.0000000000000248

Vertical Versus Horizontal Assessment Methods for Scoring Physiotherapy Entrance Interviews

2022· article· en· W4294012667 on OpenAlex
Jenna Smith‐Turchyn, Luciana Macedo, Sarah Wojkowski, Gregory F. Spadoni, Paul W. Stratford

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physical Therapy Education · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIntraclass correlationInter-rater reliabilityReliability (semiconductor)Descriptive statisticsCorrelationStatisticsPsychologyCohortMathematicsPsychometricsRating scale

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.234
GPT teacher head0.565
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it