Dietetic Staffing and Workforce Capacity Planning in Primary Health Care
Bibliographic record
Abstract
The addition of Registered Dietitians (RD) to primary health care (PHC) teams has been shown to be effective in improving health and economic outcomes with reported savings of $5 to $99 New Zealand dollars for every $1 spent on nutrition interventions. Despite proven benefits, very few Canadians have access to dietitians in PHC. This paper summarizes the literature on dietetic staffing ratios in PHC in Canada and other countries with similar PHC systems. Examples are shared to demonstrate how dietitians and others can utilize published staffing ratios to review dietitian services within their settings, identify gaps, and advocate for additional positions to meet population needs. The majority of published dietetic staffing ratios describe ranges of 1 RD: 15 000-18 500 patients, 1 RD for every 4-14 family physicians, or 1 RD for every 300-500 patients with diabetes. These staffing ratios may be inadequate as surveys report ongoing issues of limited access to dietetic counseling, under-serviced populations, and a shortage of dietitians to meet current population needs in PHC. Newer projection models based on specific population needs and ongoing workforce data are required to identify professional practice issues and accurately estimate dietetic staffing requirements in PHC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".