P22.01.A PERSON-CENTRED PRACTICE: EXPLORING FAMILY STRENGTH BY USING GENOGRAMS AND ECOMAPS
Bibliographic record
Abstract
Abstract BACKGROUND Illness of a family member impacts the entire family. Family relationships, roles and tasks often change, and neuro-oncological caregivers experience a high level of burden and distress when playing a vital role in the informal care of the patient. The theory of Family Systems Nursing advises to approach the family rather than merely the patient. Within Family System Nursing the Family Nursing Conversations is an important intervention that focus on the interactions within a family and between the nurse and the family. The Calgary Family Assessment and Intervention Model (CFAM/CFIM) guide clinical practice to be family focused. According to these models, the drawings of genograms and ecomaps are efficient tools to graphicly portray the specific family constellation and social relationships. This presentation shows how genograms and ecograms can be applied to strength the dialogue with a family when implementing family-focused conversations. MATERIAL AND Methods In the neuro-oncological outpatient clinic at the University Hospital of Copenhagen we offer family and/or caregiver conversations. At the initial conversation, a genogram and an ecogram are drawn. By visualizing the family structure and interrelationships, the family is illustrated as a unit and the family can see their constellation in relation to each other and to the outside world. RESULTS Based upon the genograms and ecograms, a dialogue takes place. They have shown to be important tools to visualize the structure, with strengths and challenges within a family system. Further, they provide a good starting point for a family-focused communication. However, to implement these tools, training is required. Therefore, we have prepared teaching material to healthcare professionals. This material has been tested in family-focused conversations. CONCLUSION The use of genograms and ecomaps have proven to be efficient for a family-focused approach in a neuro-oncological outpatient clinic. They facilitate a good starting point for a family to identify their relationships and resources. During the conversations the family/caregivers often gain new insights and management strategies for coping.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".