Experiences of Nursing Students With NANDA‐I Documentation at a Public University in Nigeria
Bibliographic record
Abstract
AIM: The study explored the lived experiences in the utilisation of NANDA International Inc. (NANDA-I) classification in the documentation of nursing care among nursing students in a selected Public University. DESIGN: A qualitative phenomenological approach was adopted, guided by a constructivist paradigm and Benner's Novice to Expert theory. METHODS: Qualitative approach was explored in this study. The second to fifth year nursing students, of similar age groups, and ward postings were included in the study. Those on part-time and first year nursing students were excluded. This was done in one phase with the use of a focus group discussion guide (FGDs). Participants were engaged in FGDs. Ethical approval was obtained from the ethical review committee, and informed consent was taken from the participants. Data were collected for 2 months and analysed using themes with independent coding by multiple researchers to ensure inter-coder reliability. The Consolidated criteria for reporting qualitative research "COREQ" was used. RESULTS: The study involved 20 participants, 15 of whom were female, with a mean age of 23 years (±2.0). Two (2) themes with various subthemes were extracted showing participants' lived experiences. These include (i) personal reflections, subthemes (Universality, Completeness, Uniformity, Decision making, and accurate documentation). (ii) Difficulty domain, subthemes (Comprehending terminologies and concepts, prioritising diagnoses, keeping up with updates and revisions of NANDA-I, and selecting appropriate 'related factor'). PATIENT OR PUBLIC CONTRIBUTION: Nursing students and nurses increase usage of the NANDA-I diagnoses, improve coordinated patient care, and should result in better quality of care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".