71. Are video interviews a good alternative to in person interviews in assessing international applicants' skills?
Bibliographic record
Abstract
We developed and administered two questionnaires to assess the interview experience of both interviewers and applicants during postgraduate medical selection interviews. Using a 5 point likert scale, the questionnaires assessed three areas (1) ability to show/assess communication, interpersonal and problem solving skills; (2) ability to know the other side well and (3) level of comfort with the interview. Interviewers and applicants were asked to provide a global rating for the interview. The questionnaires were administered to both candidates and applicants from 6 departments in 18 in-person and 12 video interviews. 30 applicant and 87 interviewer survey forms were collected and analyzed. T-tests were used to compare the means of the two groups and significance levels were analyzed. Both interviewers and applicants had a higher average global satisfaction for video interviews compared to in person interviews. No difference was indicated in the ability of interviewers to assess the applicants’ skills between the two types of interviews. For both interviewers and applicants, video interviews, compared to in person interview, had a lower average score for connecting personally & establishing rapport and for satisfaction with administrative arrangements. Video interviewed applicants had a 50% probability of getting accepted in a program compared to 22% of in person interviewed candidates. We conclude that video interviews appear to be a valuable alternative to in-person interviews, with some sacrifice in personal connection and rapport. Video interviews result in significant time and cost savings for international applicants and have potential implications for the CaRMS process as well. Sackett KM, Campbell-Heider N, Blyth JB. The evolution and evaluation of videoconferencing technology for graduate nursing education. Comput Inform Nurs. 2004 (Mar-Apr); 22(2):101-6. Shepherd L, Goldstein D, Whitford H, Thewes B, Brummell V, Hicks M. The utility of videoconferencing to provide innovative delivery of psychological treatment for rural cancer patients: results of a pilot study. J Pain Symptom Manage 2006 (Nov); 32(5):453-61. Arena J, Dennis N, Devineni T, Maclean R, Meador K. A pilot study of feasibility and efficacy of telemedicine-delivered psychophysiological treatment for vascular headache. Telemed J E Health 2004 (Winter); 10(4):449-54.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".