Bibliographic record
Abstract
In Brief The WOCNCB® is acutely aware of the increased demands on our certified nurses in all practice settings and the need for wound care specialists at all levels of nursing education, licensure, and scope of practice. This need is best illustrated when considering the aging U.S. population and the need to promote additional bedside collaborative team support for the WOCNCB® certified nurse. To help meet the increased demands for certified wound treatment nurses, the WOCNCB® has created a psychometrically sound and legally defensible certification exam for those completing the WOCN sponsored Wound Treatment Associate (WTA) program. The WTA-C® exam validates basic wound care knowledge, and principles underlying the clinical skills needed to support safe, quality, patient/family-centered care. The certification is voluntary and confirms that the non-baccalaureate prepared nurse under the guidance of a CWOCN, or a CWOCN-AP has adequate knowledge to implement effective wound treatments and pressure ulcer prevention strategies. Development of the WTA-C® represents a critical stage in the evolution of nursing-based wound care delivery. The valuable addition of this WTA-C role continues the tradition of improving outcomes at the bedside, the community and with whom all WOCNCB certified nurses touch - one patient at a time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".