Implementation und Evaluation einer familienzentrierten Pflege in der Onkologie
Bibliographic record
Abstract
Evaluation of the implementation process of family nursing in oncology Abstract. BACKGROUND: The confrontation with a life-threatening cancer disease and the resulting consequences are a great burden for patients as well as for their family members. Family nursing based on the Calgary Model was implemented on a German oncological inpatient unit in order to strengthen the family's ability to self-help. AIM: The objectives were a) to systematically record, evaluate and if necessary to modify the implementation process, b) to highlight promoting and inhibiting factors and c) to derive recommendations for transferability to other oncological units. METHODS: The implementation process was examined by means of two group interviews with nurses, five interviews with other members of the treatment team, and observations of, in each case four, family assessments, family interviews and family-related team meetings. RESULTS: Family nursing could be implemented in a modified form. Genograms and ecomaps have become part of the admission interview. In family interviews, needs of the entire family were determined with the help of circular communication. Family-related team meetings were carried out according to an adapted method of the reflecting team. The complete implementation of family nursing was impeded by the lack of professional consulting competences of the nursing staff, the system of nursing care delivery and lack of time. CONCLUSION: An implementation of family nursing in other oncological units is recommended under modified preconditions.
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 | 0.042 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".