Finnish nurses’ views of support provided to families about traumatic brain injury patients’ daily activities and care
Bibliographic record
Abstract
Background: Large numbers, almost eight million, of brain injuries are diagnosed worldwide annually. Social support (informational, emotional and practical) has been identified as essential for helping members of TBI patients’ families to cope with the severe situations these injuries cause. We have assessed nurses’ views of the support provided in Finland. Methods: The target group included all nursing staff (n = 172) of neurosurgical wards in Finland. Data were collected during 2010, from 115 of these nurses working in neurosurgical wards of all five Finnish university hospitals. The response rate was 67 %. The data were analysed (using SPSS version 17 software) by calculating descriptive statistics, applying Kolmogorov-Smirnov tests, and ANOVA (one- and two-way), MANOVA and linear regression analyses. Results: The results indicate that nurses’ education affects the practical support they provide to TBI patients' family members: registered nurses considered themselves most likely to take into account issues related to liaison with family members. The length of work experience was related to how often nurses reported discussing mood swings and other TBI symptoms with family members. Conclusions: Providing practical support to TBI patients’ family members requires nurses to possess multidimensional practical competences related to the symptoms caused by the brain injury.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".