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Record W4281482565 · doi:10.1097/jac.0000000000000424

Employers' Perspectives on the Use of Medical Assistant Apprenticeships

2022· article· en· W4281482565 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Ambulatory Care Management · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsApprenticeshipWorkforceVariety (cybernetics)BusinessHealth careWorkforce developmentMedical educationQualitative researchPublic relationsNursingMedicineEconomic growthPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Medical assistants (MAs) are among the fastest-growing occupations in the United States, yet health care employers report high turnover rates and difficulty filling MA positions. Employers are increasingly using apprenticeship to meet emerging workforce needs. This qualitative study examined the perspectives of 14 employers using registered MA apprenticeships in 8 states. The findings revealed motivations for using apprenticeship, perceived benefits to the organization, challenges with implementation, and reflections on successful implementation. We detail how MA apprenticeship is successfully meeting recruitment and training needs in a variety of health care organizations, especially where program support resources are available.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.104
GPT teacher head0.408
Teacher spread0.305 · 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