The 2005 Schering Lecture: Touched by a nurse: The imprint of exemplary oncology nursing care
Bibliographic record
Abstract
This paper examines the relationship between exemplary oncology nursing practice and professional fulfillment. What inspires nurses to choose oncology and, subsequently, what gives them impetus to continue in this field? To answer this question, oncology nurses were invited to recall moments in their careers when they were satisfied that they had chosen the "right" career. Data, in narrative form, were collected through an online research technique. Submissions were analysed using three approaches, narrative analysis (Priest, Roberts, & Woods, 2003), poetic interpretation (van Manen, 1990) and photovoice (Woolrych, 2004). Findings reveal that oncology nurses who provide excellent care, and make strong connections with their patients, are also usually very satisfied with their careers. Specifically, nurses provide exemplary care and report attaining professional fulfillment when they achieve connection with those in their care by affirming value and sharing humour. Second, caregivers feel they are making a difference when they "see patients through" the care trajectory. Nurses accomplish this in part by helping people live on, individualizing care, enabling hope, and helping individuals find meaning. It is anticipated that this paper will reawaken memories of similar experiences in caregivers, thus enhancing confidence, self-esteem and energy and reminding nurses that they do unquestionably leave an imprint.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".