“Hello. May I speak with someone, please? It's not about my physical pain.”: A retrospective study about the factors associated with phone calls to a Portuguese home-based palliative care team
Bibliographic record
Abstract
OBJECTIVE: Telephone availability is integrated into our home-based palliative care team (HPCT) with the aim of helping terminally ill patients and their caregivers alleviate their physical and psychosocial suffering, in addition to the team's home visits. We aimed to compare the differences between non-callers (patients with no phone calls during the team's follow-up period) vs. callers (≥1 phone call during the team's follow-up period) across sociodemographic, clinical, physical, and psychosocial variables. METHOD: Retrospective analysis of all patients with and without phone call entries registered in our anonymized database, from October 2018 to September 2020. RESULTS: We analyzed 389 patients: 58% were male, and the average age was 71 years old; 84% had malignancies, with a mean palliative performance status of 45%. The majority of patients (n = 281, 72%) made at least one phone call to HPCT. On average, a mean of 2.5 calls (SD = 3.61; range: 0-26) per patient was registered. Callers compared with non-callers more frequently lived with someone (p = 0.030), preferred home as a place to die (p = 0.039), had more doctor (p = 0.010) and nurse home visits (p = 0.006), a prolonged HPCT follow-up time (p = 0.053), along with more frequent emergency room visits (p < 0.001) and hospitalizations (p = 0.043). Moreover, those who made at least one phone call to the HPCT had a higher frequency of conspiracy of silence (p = 0.046), anxiety (p = 0.044), and lower palliative performance status (p = 0.001). No statistically significant associations or differences were found for the other variables. SIGNIFICANCE OF RESULTS: Several factors seem to correlate with an increased number of phone calls, and physical suffering does not play a relevant role in triggering contacts, in contrast with psychosocial and other clinical factors.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".