Occupational Health Nursing
Bibliographic record
Abstract
As a certifying body for occupational health nurses in the United States and Canada, the American Board for Occupational Health Nurses, Inc. (ABOHN) must ensure its certification examinations validly reflect current occupational health nurse practice. This report presents information from the ABOHN 2004 practice analysis. The study's primary purpose was to analyze areas of knowledge, skill, and ability for occupational health nurses as reflected by the tasks they perform to guide refinement of ABOHN's certification examinations. A valid and reliable survey instrument, containing demographic and job-related questions and 172 task statements was developed. A total of 5,586 surveys (4,921 Web-based and 665 paper) were made available to occupational health nurses throughout the United States and Canada. The usable response rate was 23.5% (N = 1,223). Decision rules were used to determine which survey tasks were appropriate for inclusion in Certified Occupational Health Nurse (COHN) and Certified Occupational Health Nurse Specialist (COHN-S) certification examination blueprints. The revised blueprints were used to develop new examinations. Study data also validated the existing ABOHN Case Management (CM) specialty examination blueprint, and verified occupational health nurse roles and responsibilities related to safety programs. Based on analysis of the safety-related items, ABOHN in collaboration with the Board of Certified Safety Professionals, has created a safety management credential (SM) and associated examination that certified occupational health nurses may use to verify their safety role proficiency.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.179 | 0.084 |
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".