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Record W2004412902 · doi:10.1097/acm.0b013e31819047bf

Commentary: The Right Time to Rethink Part-Time Careers

2008· review· en· W2004412902 on OpenAlexaff
Valerie A. Palda, Wendy Levinson

Bibliographic record

VenueAcademic Medicine · 2008
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMuscular Dystrophy CanadaUniversity of Toronto
Fundersnot available
KeywordsDemographicsProductivityTime managementFull-timePart-time employmentQuality (philosophy)Work (physics)Freedom of choiceResource (disambiguation)Medical educationPsychologyPublic relationsMedicinePolitical scienceSociologyManagementComputer scienceEngineeringEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The demand for part-time academic positions is bound to increase because of the changing demographics of medicine and the needs of both women and men faculty. One of the main benefits of working part-time is the freedom to shape a career that is tailored to one's individualized life needs. Studies indicate that part-time faculty may enhance quality of care, patient satisfaction, resource utilization, and productivity. Division chiefs and department chairs who have flexible hiring policies to meet the needs of part-time faculty are likely to be more successful in recruitment and retention. The authors describe some of the benefits and drawbacks of part-time work, and they offer advice for faculty members seeking part-time careers and for leaders seeking to employ them.

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)0.008
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.036
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.004
Science and technology studies0.0030.005
Scholarly communication0.0030.008
Open science0.0090.002
Research integrity0.0360.026
Insufficient payload (model declined to judge)0.0130.014

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 head0.065
GPT teacher head0.360
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2008
Admission routes1
Has abstractyes

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