The impact of COVID‐19 on dental school admissions: A student and faculty perspective
Bibliographic record
Abstract
Since the COVID-19 pandemic declaration by the World Health Organization in March 2020, many North American dental programs were forced to abandon their traditional in-person interviews in favor of online interviews, and adjust the application process in response to imposed limitations. In this perspective, we discuss the perceived impact of COVID-19 on applicants and the application process to dental school while offering recommendations for the future. Dental school admission teams needed to pivot quickly to ensure a fair process while maintaining established standards of admissions.1 Most schools were forced to cancel or modify in-person interviews and resorted to online interviews. Simultaneously, many dental schools saw higher application numbers. The virtual interviews were more accessible to candidates and many students may have benefited due to being in their ambient environments. However, it was more difficult for admissions committees to obtain the same level of information about a candidate through online interaction.1 Technical glitches, environmental interruptions, and variations in access to reliable high-speed Internet on both the interviewer and interviewee side may have impacted performance and assessment. Selection committee members may have been concerned about the lack of appropriate oversight in the virtual space and the potential for misconduct. Applicants have been dealing with lost summer jobs, internships, and research opportunities due to pandemic restrictions. Harris et al. reported on income disparities in the applications received by dentistry programs, and many resource-scarce applicants who have been financially impacted.2 Marginalized and rural applicants from lower socio-economic backgrounds may have chosen not to apply in the 2020-2021 cycle due to financial burdens of the pandemic and lack of access to reliable, fast-paced Internet service, in addition to other perceived barriers, which compromised dental schools’ mission to increase student diversity.3 Additional restrictions caused by the pandemic meant that many prospective applicants were unable to shadow dentists in 2020 and 2021, and therefore compromised their ability to learn about dentistry and demonstrate their suitability for dental practice during the application and interview phases. Many students did not get a chance to tour the dental school/campus during the 2020-2021 application cycles and missed out on important interactions with current students, faculty, alumni, and future peers. Standardized tests were canceled, delayed, or rescheduled creating additional financial and mental health stressors. Despite some students who saw their grades improve during the virtual-online mode of education, many applicants’ grades were also negatively impacted during the 2020-2021 academic year because of change in the grading system, change in class delivery mode, and objective hardships such as helping sick family members, being sick or quarantined, dealing with grief, and other mental health concerns linked to COVID-19.4 Some students did not meet the lab requirement of some pre-dental courses due to quarantine restrictions.5 The COVID-19 pandemic has contributed to difficulties and challenges for both dental applicants and admissions committees in North America. Applicants may have benefited from the reduced cost of online interviews while some may have benefited from the online dental interviews and seen a boost in grades during the virtual mode of education. But many students were negatively impacted by the lack of student jobs, internships, dental shadowing experiences, disruption in Dental Admission Test (DAT) administration, unfavorable course delivery, and grading policies, and missed opportunities to visit the dental schools, in addition to objective pandemic-related social and personal hardships. It is important for dental admissions committees to acknowledge and adapt to these challenges in upcoming application cycles due to the long-term impact of the pandemic on higher education, students, and their support system. Specifically, dental school admissions committees could adjust the course/lab requirement of the required Science courses, offer flexibility in dental shadowing minimum requirement, waive the secondary admission fees based on financial needs which can also benefit the underrepresented groups that have been more negatively impacted by the pandemic, and extend the DAT score acceptability beyond current time limitations. Dental schools administrators should advocate for a fee reduction of DAT costs (American and Canadian Dental Associations), as well as for enhanced financial support by ADEA in the form of additional fee waivers for applicants in need.6
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 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".