Using social exchange theory to understand non-terminal palliative care referral practices for Parkinson’s disease patients
Bibliographic record
Abstract
BACKGROUND: A palliative approach is recommended in the care of Parkinson's disease patients; however, many patients only receive this care in the form of hospice at the end of life. Physician attitudes about palliative care have been shown to influence referrals for patients with chronic disease, and negative physician perceptions may affect early palliative referrals for Parkinson's disease patients. AIM: To use Social Exchange Theory to examine the association between neurologist-perceived costs and benefits of palliative care referral for Parkinson's disease patients and their reported referral practices. DESIGN: A cross-sectional survey study of neurologists. SETTING/PARTICIPANTS: A total of 62 neurologists recruited from the National Parkinson Foundation, the Medical Association of Georgia, and the American Academy of Neurology's clinician database. RESULTS: Participants reported significantly stronger endorsement of the rewards ( M = 3.34, SD = 0.37) of palliative care referrals than the costs ( M = 2.13, SD = 0.30; t(61) = -16.10, p < 0.0001). A Poisson regression found that perceived costs, perceived rewards, physician type, and the number of complementary clinicians in practice were significant predictors of palliative care referral. CONCLUSION: Physicians may be more likely to refer patients to non-terminal palliative care if (1) they work in interdisciplinary settings and/or (2) previous personal or patient experience with palliative care was positive. They may be less likely to refer if (1) they fear a loss of autonomy in patient care, (2) they are unaware of available programs, and/or (3) they believe they address palliative needs. Initiatives to educate neurologists on the benefits and availability of non-terminal palliative services could improve patient access to this care.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".