Readying the Workforce
Bibliographic record
Abstract
BACKGROUND: Veterans Health Administration (VHA) primary care providers (PCPs) often see few women, making it challenging to maintain proficiency in women's health (WH). Therefore, VHA in 2010 established Designated WH Providers, who would maintain proficiency in comprehensive WH care and be preferentially assigned women patients. OBJECTIVE: To evaluate early implementation of this national policy. METHODS: At each VHA health care system (N=140), the Women Veterans Program Manager completed a Fiscal Year 2012 workforce capacity assessment (response rate, 100%), representing the first time the national Designated WH Provider workforce had been identified. Assessment data were linked to administrative data. RESULTS: Of all VHA PCPs, 23% were Designated WH Providers; 100% of health care systems and 83% of community clinics had at least 1 Designated WH Provider. On average, women veterans comprised 19% (SD=27%) of the patients Designated WH Providers saw in primary care, versus 5% (SD=7%) for Other PCPs (P<0.001). For women veterans using primary care (N=313,033), new patients were less likely to see a Designated WH Provider than established women veteran patients (52% vs. 64%; P<0.001). CONCLUSIONS: VHA has achieved its goal of a Designated WH Provider in every health care system, and is approaching its goal of a Designated WH Provider at every hospital/community clinic. Designated WH Providers see more women than do Other PCPs. However, as the volume of women patients remains low for many providers, attention to alternative approaches to maintaining proficiency may prove necessary, and barriers to assigning new women patients to Designated WH Providers merit attention.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.104 | 0.031 |
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".