Bibliographic record
Abstract
This article discusses a study exploring the lived experience of family nursing for novice registered nurses. There has been an increased emphasis on including family content in Canadian nursing education curricula. Literature on family nursing is ambiguous about differentiating family nursing at the generalist and specialist level, and acknowledges that there is a blurring of lines between the two. The study utilized a phenomenological approach to examine how nurses with 2 years or less in practice experience family nursing in a variety of settings. Following ethical approval, invitations were sent to all nurses employed in two health authorities, who met the study criteria. Five nurses were interviewed using a semistructured interview. Participants shared how they practice family nursing in the current nursing situation of shortages and constraints. This study adds to our understanding of what happens at a beginning level of family nursing, how nurses understand and experience caring for families in the everyday enactment of their professional role, and barriers and facilitators to including family in nursing care. The findings provide important information for nurse educators in grounding the teaching of family nursing in the real world of nurses.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".