Exploring social media and admissions decision-making – friends or foes?
Bibliographic record
Abstract
BACKGROUND: Despite the ever-increasing use of social media (e.g., Facebook, Twitter) little is known about its use in medical school admissions. This qualitative study explores whether and how social media (SM) is used in undergraduate admissions in Canada, and the attitudes of admissions personnel towards such use. METHODS: Phone interviews were conducted with admissions deans and nominated admissions personnel. A qualitative descriptive analysis was performed using iterative coding and comparing, and grouping data into themes. RESULTS: Personnel from 15 of 17 Canadian medical schools participated. A sizeable proportion had, at some point, examined social media (SM) profiles to acquire information on applicants. Participants did not report using it explicitly to screen all applicants (primary use); however, several did admit to looking at SM to follow up on preliminary indications of misbehaviour (secondary use). Participants articulated concerns, such as validity and equity, about using SM in admissions. Despite no schools having existing policy, participants expressed openness to future use. CONCLUSIONS: While some of the 15 schools had used SM to acquire information on applicants, criteria for formulating judgments were obscure, and participants expressed significant apprehension, based on concerns for fairness and validity. Findings suggest participant ambivalence and ongoing risks associated with "hidden" selection practices.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".