Bibliographic record
Abstract
Diagnosing is the second phase of the nursing process. In this phase, nurses use critical-thinking skills to interpret assessment data and identify client strengths and problems. Diagnosing is a pivotal step in the nursing process. Activities preceding this phase are directed towards formulating the nursing diagnoses; the care-planning activities following this phase are based on the nursing diagnoses (see Figure 12.1). The identification and development of nursing diagnoses began formally in 1973, when two faculty members of Saint Louis University, Kristine Gebbie and Mary Ann Lavin, perceived a need to identify nurses' roles in an ambulatory care setting. The First National Conference to identify nursing diagnoses was sponsored by the Saint Louis University School of Nursing and Allied Health Professions in 1973. Subsequent national conferences occurred in 1975, in 1980, and every two years thereafter. International recognition came with the First Canadian Conference in Toronto in 1977 and the International Nursing Conference in May 1987 in Calgary, Alberta, Canada. In 1982, the conference group accepted the name North American Nursing Diagnosis Association (NANDA), recognising the participation and contributions of nurses in the United States and Canada. In 2002, NANDA became NANDA International (NANDA-l) to reflect increasing worldwide interest in the field of nursing diagnosis terminology. NANDA International has approved more than 200 nursing diagnoses for clinical use, testing and refinement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.099 |
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".