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Record W3176017254 · doi:10.1002/hon.52_2880

A CROSS‐SECTIONAL STUDY ON THE GLOBAL DIFFERENCES IN INFORMATION EXPERIENCES AND NEEDS OF PATIENTS WITH LYMPHOMA AND CLL

2021· article· en· W3176017254 on OpenAlexaff
Lorna Warwick, Olufunmilayo A Bamigbola, Natalie Dren

Bibliographic record

VenueHematological Oncology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsLeukemia & Lymphoma Society of Canada
Fundersnot available
KeywordsLymphomaMedicineEnvironmental healthCross-sectional studyOncologyInternal medicineDermatologyPathology

Abstract

fetched live from OpenAlex

Background: Patient-centricity remains a cornerstone in the care of patients with lymphoma and CLL, as informed patients are consistently associated with better outcomes and healthcare experiences. Aims: This study uses the Lymphoma Coalition (LC) 2020 Global Patient Survey (GPS) on Lymphomas and CLL to describe the global differences in patients’ information experiences at diagnosis, as well as to compare the areas of need for more information. Methods: Globally, 9,179 patients with lymphoma or CLL from 89 countries took part in the LC 2020 GPS. The countries were grouped into regions, and regions with greater than 200 patient respondents were included in the analysis. The five regions analysed were Asia (AS) (n = 2326), Oceania (OC) (n = 695), Europe (EU)(n = 4343), North America (NA) (n = 1543), and South America (SA) (n = 214). Descriptive analyses of questions relating to patients’ information experiences at diagnoses and areas in which they needed more information were performed in IBM SPSS v27. Results: All the regions differed significantly (p < 0.05) in the demographic categories of age, sex, education level, and household status. When asked which time point patients had the greatest need for information, over half of patients in all the regions reported the time point as ‘within the first month following diagnosis’ (AS-62%, OC-58%, EU-57%, NA-53% and SA-59%) (Table 1). Relating to how patients felt about the amount of information they were given upon diagnosis with lymphoma, patients from AS were the most prevalent in reporting they were not given enough information (55%) followed by patients from NA (36%). Additionally, only 30% of patients from AS reported receiving the right amount of information, while 60% and more, of patients from NA, EU, SA, and OC reported the same (60%, 67%, 71% and 70% respectively) (Table 1). When asked about the specific areas patients needed more information in, the most commonly reported areas in all the regions were ‘treatment options’ (AS-76%, OC-44%, EU-50%, NA-61% and SA-40%), ‘diagnosis and what it means’ (AS-58%, OC-45%, EU-56%, NA-51% and SA-38%), and ‘treatment side-effects’ (AS-61%, OC-44%, EU-45%, NA-38% and SA-41%). Patients also reported needing information on ‘support for self care’, ‘psychological support’, ‘support for their families’, and ‘fertility’ (Table 1). Only 2% of patients from AS reported not needing any additional information compared to the other regions (OC-19%, EU-11%, NA-16% and SA-18%) (Table 1). Conclusion: Access to timely and credible medical information remains an essential aspect of a successful patient experience and this study shows that patients with lymphoma have diverse information experiences and needs. It is therefore important that doctors provide information that address(es) each patient's unique information needs. In the future, LC would like to explore how demographic differences may have confounded results. Keywords: Cancer Health Disparities Conflicts of interests pertinent to the abstract L. Warwick Research funding: Takeda, Pfizer and Abbvie O. Bamigbola Research funding: Takeda, Pfizer and Abbvie N. Dren Research funding: Takeda, Pfizer and Abbvie

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.432
Teacher spread0.366 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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