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Record W10268684

Perceptions, Motivations, and Concerns of Post-Secondary Faculty Regarding Implementing Service-Learning Pedagogy Into Curriculum

2008· article· en· W10268684 on OpenAlexvenueaboutno aff
Cynthia Smith

Bibliographic record

VenueJournal of the Canadian Dietetic Association · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
FundersUniversity of Central Florida
KeywordsPedagogyCurriculumService-learningPerceptionService (business)PsychologyMedical educationSociologyBusinessMedicine
DOInot available

Abstract

fetched live from OpenAlex

Burnaby Hospital recently implemented a computerized Food Management System. The system is fully operational in the Extended Care Unit (ECU) and now is being introduced in the Acute Care Unit (ACU). The main goals for the new program were: cost savings, more effective use of foodservice employee time and skills, and better patient care. To date, the total time saved by use of the computerized system has been equivalent to one full time employee. The ECU dietitian's and the dietary technician's duties have been upgraded. The technician has been relieved of clerical tasks and has taken over some responsibilities formerly performed by the dietitian; the dietitian is able to perform more administrative and planning tasks; and the former ECU coordinator is manager of patient services and responsible for coordinating changes in the computer system. Research indicates that this system, designed and developed for Burnaby Hospital by Rick Hepting & Associates and running on an MAI Basic Four min computer, model 210, is unique among North American hospitals. Current applications include manipulation of patient diet information for automatic menu processing, food tallying, and production of nourishment labels. Future enhancements will take care of production reporting, standard recipe printouts, food cost analysis, and nutrient analysis. The system has valuable potential for use in other hospitals.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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.

Study designObservational
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
Published2008
Admission routes2
Has abstractyes

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