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Patient and caregiver-reported acceptability of an automatic phone call offering supportive and palliative care referral for advanced non-small cell lung cancer patients.

2022· article· en· W4286295316 on OpenAlexafffund
Aliyah Pabani, Seema King, Sadia Ahmed, Lisa Shirt, Vanessa Slobogian, Chandra Vig, Desirée Hao, Lisa Barbera, Elizabeth Kurien, Maria Santana, Patricia Biondo, Aynharan Sinnarajah, Jessica Simon

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreAlberta Health ServicesUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicinePalliative careReferralFamily medicinePsychological interventionLung cancerLikert scaleDistressNursingInternal medicine

Abstract

fetched live from OpenAlex

e24097 Background: Timely palliative care interventions can help to alleviate the distress people experience after a diagnosis of an incurable, life-threatening cancer. However, referrals to palliative care continue to be late due to various provider and patient barriers. The Palliative Care Early and Systematic (PaCES)-Automatic study was co-designed with patients and providers and implemented an early palliative care intervention for newly diagnosed stage IV non-small lung cancer (NSCLC). The objective of this study was to determine patient/caregiver-reported acceptability of a phone call from a supportive and palliative care (SPC) nurse offering consultation, automatically (without referral) after first oncologist appointment. Methods: Two SPC specialist nurses screened out-patient clinic lists at a tertiary cancer center weekly and called all eligible patients offering an in-home consultation. Eligibility: > 18 years, newly diagnosed/suspected Stage IV NSCLC and had first medical/radiation oncologist visit. Patients/caregivers were surveyed about the acceptability (5-point Likert scale) of the automatic phone call offering a palliative care consult, using Sekhon’s Framework of Acceptability domains. Results: Among the 113 patients screened, 81 patients/caregivers were contacted and offered SPC consultation and 72% accepted the in-home consult. Of 70 patients/caregivers that agreed to be contacted for the survey: 4 did not recall the call offering SPC, 3 declined participating in the survey, and 15 were not reached. Of 48 respondents, 93.6% rated overall acceptability of the automatic call offering SPC consultation somewhat/completely acceptable, with the other 6.4% rating it as neither acceptable nor unacceptable. Of 35 patients/caregivers that completed the full survey: 31% caregivers, 63% female, 57% ≤65 years, 29% ≤high school education, 67% (n = 27) < $60,000 household income, 80% spoke only English/French, and 71% Caucasian. Within the domains of acceptability, 94.7% were comfortable receiving the call, 91.9% understood why they received it; 86.5% thought the call was a valuable; 69.5% thought the call helped them; 65.7% learned about SPC from the call; no one expressed concern that the SPC nurse had access to their contact/health information; 97.2% thought the call didn’t take much physical/emotional effort and were confident in their ability to participate (ask questions/make decisions). Conclusions: Nearly all patients/caregivers found the automatic SPC call offering consultation to be acceptable. Most patients agreed to the consultation offer. Routine calls offering SPC consultation may be a timely alternative to awaiting conventional referral by oncologists.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.167
GPT teacher head0.505
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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