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Record W2142173279 · doi:10.1034/j.1600-0579.6.s3.3.x

1.1 Student selection and the influence of their clinical and academic environment on learning

2002· article· en· W2142173279 on OpenAlexaff
Peter Gaengler, Johann de Vries, Llze Akota, Irena Balčiūnienė, Peter Berthold, Maria Gajewska, David C. Johnsen, Ilga Urtâne, Laurence J. Walsh, Alies Zijlstra

Bibliographic record

VenueEuropean Journal Of Dental Education · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSelection (genetic algorithm)BenchmarkingVariety (cybernetics)Process (computing)Medical educationDental educationValue (mathematics)PsychologyOutcome (game theory)Higher educationMathematics educationPedagogyMedicinePolitical scienceComputer scienceBusinessMarketing

Abstract

fetched live from OpenAlex

Student selection and recruitment play a vital role in the successful outcome of dental education. To identify key issues and practices in selection and recruitment, the group assessed current processes, philosophies and practices from a range of different educational systems, although it was not possible to gather data from all countries or continents within the timeframe provided. Furthermore, the group explored the effect of the educational learning environment on the successful outcome of teaching dental students. It is clear that a wide variety of practices and philosophies exist and are used in different parts of the world. Measuring the success of any given process used for student selection remains a challenge. In some parts of the world, certain practices have become an integral part of the tertiary educational system, and have been applied in a similar way by many or all of the dental schools in that country. In other countries, methods vary from one dental school to another, often reflecting differences in the structure and philosophy of the educational system. There was great variation in the combinations of selection criteria used and in student recruitment strategies. However, it was clear that there was much to be gained by learning from the experiences of other dental schools in student selection. Lessons learned, best practices and philosophies used and supporting value systems proved to be very helpful for benchmarking the processes used. In the discussion of student selection, a number of important questions were raised which deserve further thought and reflection both in the ongoing debate and as part of the ever-changing world of dental education. Important new matters that require more debate and research include: a) ethical issues, including the nature of funding from the student perspective, and the concern that in some regions dentistry may become a profession only for the elite or wealthy students. b) Health standards of students entering dental school. c) How realistic is the applicant's sense of dentistry as a profession? d) How accurate is the students' sense of their career opportunities and the employment market upon graduation? Finally, the over-arching question remains, how valid, reliable and predictable are existing selection practices? Will it be practical and meaningful to standardize methods used, or will exchanging ideas as part of this global debate assist the thought process of dental leaders to improve selection practices by learning from the experiences of other schools in different parts of the world? The processes of open debate, sharing ideas and opinions and identifying sound practices across the globe is a powerful catalyst for developing innovative answers to the complex problems posed by student selection and recruitment. A 'virtual' global process with wide input from as many dental schools as possible should improve the efficacy of student selection, and allow dental educators to identify the 'potential' of prospective students and predict more accurately dental student outcomes. The debate that we have started will certainly contribute to providing a knowledge base which dental educators will be able to draw on when reviewing selection processes in their own schools.

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 imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.080
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0110.004
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.037
GPT teacher head0.361
Teacher spread0.324 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2002
Admission routes1
Has abstractyes

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