Attrition From Premedical Studies Among Latinas: Case
Bibliographic record
Abstract
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
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".