2789 Patient navigator coordination of transitions for older adults with fractures: family caregiver experiences
Bibliographic record
Abstract
Abstract Introduction Fall-related injuries such as fractures are on the rise as the older adult population grows in New Brunswick, Canada. These injuries can lead to hospitalisation and transitions in care that are complicated for patients and families. The objective was to investigate the impact of patient navigators (PNs) working alongside the healthcare team on patient and family experiences, as compared to the usual standard of care (SOC), for adults aged 65 and older admitted with a fracture to an Orthopaedic Unit at one hospital in New Brunswick. Methods A concurrent embedded mixed methods design, in which the quantitative randomised control trial had an embedded qualitative component. The results for the family caregiver qualitative component, which used an interpretive description approach, are presented. Results Semi-structured interviews were conducted and thematically analysed for 15 family caregivers (8 PN group, 7 SOC group). The SOC caregivers, six women and one man, had a mean age of 64.6 years (SD = 6.9 years). The mean age of the 8 women in the PN group was 61.3 years (SD = 10.1). All participants in both SOC and PN groups self-reported their ethnicity as white. Thematic analysis found that SOC group caregivers discussed patients relying on support from family and friends throughout their care journey, whereas caregivers in the PN group predominantly discussed finding PNs supportive and helpful. Both groups discussed the ongoing stress that they felt throughout the care journey of the patient for which they cared for; however, for the PN group this topic was less prevalent. Conclusions This study provides an understanding of the positive impacts a patient navigator can have on older adult inpatient care and transitions in care. Patient Navigators were shown to be helpful to families, particularly those of patients with higher care needs and fewer family supports.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".