Computerized documentation and community health nursing students
Bibliographic record
Abstract
Background/Objective: Proficiency in computerized documentation systems is an essential element of most areas of nursing practice today. Community health is one example of an area of nursing practice where computerized documentation systems help in the provision of high quality care. Nursing students must learn the basic principles of and begin to participate in the practices of computerized nursing documentation. It is, therefore, the responsibility of nursing faculty to promote student involvement in this important process. Methods: Two different faculty experiences with students participating in computerized nursing documentation were described using different electronic systems, a notebook computer system and a Personal Digital Assistant (PDA) system. Results/Conclusions: After reviewing the results of this descriptive experience, it is recommended that before students participate in computerized documentation, they receive written instructions. Sample charts, practice under direct faculty and staff guidance, and standardizing the learning experience are imperative. Educating the student in a technological environment is no longer optional for nurse faculty as the accurate documentation, transmission and management of data assures that the best practices are maintained, the proper billing of visits can be ensured, and the communication between the nursing student and community health nurses, as well as all members of the multi-disciplinary team is fostered.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".