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Record W2898691735 · doi:10.3148/67.0.2006.s30

<i>Estimation of Human Resource Needs And Cost of Adding Registered Dietitians</i> To Primary Care Networks

2006· article· en· W2898691735 on OpenAlexaffvenueabout
Julia Witt, Paula Brauer, Linda Dietrich, Bridget Davidson

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2006
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEstimationMedicineHuman resourcesPromotion (chess)Primary careHuman servicesPopulationHealth planHealth careFamily medicineOperations managementEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: Information on human resources and costs is needed to plan for the addition of registered dietitian (RD) services to new models of primary health care (PHC). Estimates were developed, based on an analysis of an enhanced RD model of counselling and health promotion services in three Ontario Family Health Networks (FHNs). METHODS: Both direct and indirect costs were averaged over the three FHNs. Costs and RD activities were tracked throughout 2005. The FHN staff completed two questionnaires addressing communication, case management, and satisfaction with RD services. RESULTS: Actual and reported case management indicated that an estimated 1.3% to 2.4% of the 60,000 enrolled patients may require individual nutrition counselling in a year. If one full-time equivalent (FTE) RD can manage 380 new referrals, then one FTE RD is needed per 15,800 to 29,000 patients. The estimated direct costs of adding one FTE RD (including expenses and fixed costs) is US dollars 78,169 to US dollars 80,169, when the RD is an independent contractor. CONCLUSIONS: Additional studies are needed to develop better estimates of human resource needs and costs of interdisciplinary nutrition services in all PHC settings. These estimates should be based on population characteristics and direct and indirect costs for all models of nutrition services in PHC settings.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.108
GPT teacher head0.446
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations15
Published2006
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Dietetic Practice and ResearchSame topicDietetics, Nutrition, and EducationFrench-language works237,207