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Record W7100588053

Attrition From Premedical Studies Among Latinas: Case

2016· article· en· W7100588053 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionUnderrepresented MinorityQuarter (Canadian coin)African americanCareer PathwaysCareer pathMedical school
DOInot available

Abstract

fetched live from OpenAlex

Minorities are significantly underrepresented in the U.S. physician workforce. Female minorities wishing to become doctors face additional gender barriers, since women who enter college with the desire of becoming physicians are more likely than men to exit programs of undergraduate studies (“premed studies”) that lead to medical school. To explore the factors that may cause minority women to lose interest in a medical career, in-depth one-to-one interviews were conducted during the first quarter of freshman year with minority women who had said prior to matriculating that they were interested in pursuing a career in medicine. Interviews with seven Latinas were analyzed to develop grounded theory to identify potential causes of attrition from premedical studies within this underrepresented group. Findings point to the importance of linking such students into premedical-student support systems at the beginning of their freshman year to retain them in the premedical pipeline and argue that the mechanisms that push students out of the “premed pipeline ” operate at the institutional level to passively, rather than actively, discourage premedical students ’ career ambitions. 378129 HJB32410.1177/0739986310378129Gonzal ez et al.Hispanic Journal of Behavioral Sciences

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 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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.339
Teacher spread0.221 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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