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Record W4295365135 · doi:10.29074/ascls.2019001941

Integrating Biochemistry Into a Program Serving Multiple Tracks in Medical Laboratory Science

2019· article· en· W4295365135 on OpenAlexaff
Suzanne Carpenter, Keith Belcher, Charlotte Bates, Dean Earlix

Bibliographic record

VenueAmerican Society for Clinical Laboratory Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsSpringboard (Canada)
Fundersnot available
KeywordsMedical educationCurriculumCertificationQuality (philosophy)UnavailabilityLicenseComputer scienceChemistryPsychologyEngineeringMedicinePedagogy

Abstract

fetched live from OpenAlex

<h3>ABSTRACT</h3> This education case study addresses 2 issues: the program-identified need for more biochemistry content in the prerequisite course, Organic Chemistry I, and its availability to the Medical Laboratory Science Online Career Ladder track students. The underlying principles for resolving the issues are the constraints of the program curriculum and the unavailability of an online organic chemistry course at Georgia Southern University. The first step in addressing the issues involved collaboration between the Medical Laboratory Science and Chemistry faculty members to identify the specific biochemistry topics on which program courses build. Because the Biochemistry I course has the prerequisites of 2 semesters of Organic Chemistry and the medical laboratory program of study only had room for 1 course in this area, a new course was designed to address the program-identified content need. The new course was offered in a face-to-face format, but one of the program tracks utilizes entirely online instruction. To allow those students access to the new course, the course instructor obtained e-Faculty status and undertook designing an online version. The process included the expertise of an instructional designer, and the resulting online course was rich in content and teaching strategies to provide the social presence necessary for student engagement in that venue. The online course immediately earned Quality Matters Certification and has been offered 3 times to date. Forty-one students have taken the new course. Of those eligible to apply, 71% entered the Medical Laboratory Science program, and 78% of the entrants have graduated or are on track to do so. The average course grade point average data for the 5 cohorts reveal that the strong level of student success in the face-to-face course has not been observed in the online course (3.55 and 2.53, respectively), requiring continued efforts to deliver content, engage students, and assess learning in alternative ways.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0050.003
Open science0.0030.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.044
GPT teacher head0.492
Teacher spread0.449 · 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
GenreMethods

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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