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Record W4392579740 · doi:10.1200/op.23.00715

Perceived Barriers Toward Patient-Reported Outcome Implementation in Cancer Care: An International Scoping Survey

2024· article· en· W4392579740 on OpenAlex
Lawson Eng, Raymond J. Chan, Alexandre Chan, Andreas Charalambous, HS Darling, Lisa Grech, Corina van den Hurk, Deborah Walker, Sandra A. Mitchell, Dagmara Poprawski, Elke Rammant, Imogen Ramsey, Margaret I. Fitch, Yin Ting Cheung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJCO Oncology Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineWorkflowMultinational corporationFamily medicineFinanceBusinessDatabase

Abstract

fetched live from OpenAlex

PURPOSE Implementation of patient-reported outcomes (PROs) collection is an important priority in cancer care. We examined perceived barriers toward implementing PRO collection between centers with and without PRO infrastructure and administrators and nonadministrators. PATIENTS AND METHODS We performed a multinational survey of oncology practitioners on their perceived barriers to PRO implementations. Multivariable regression models evaluated for differences in perceived barriers to PRO implementation between groups, adjusted for demographic and institutional variables. RESULTS Among 358 oncology practitioners representing six geographic regions, 31% worked at centers that did not have PRO infrastructure and 26% self-reported as administrators. Administrators were more likely to perceive concerns with liability issues (aOR, 2.00 [95% CI, 1.12 to 3.57]; P = .02) while having nonsignificant trend toward less likely perceiving concerns with disruption of workflow (aOR, 0.58 [95% CI, 0.32 to 1.03]; P = .06) and nonadherence of PRO reporting (aOR, 0.53 [95% CI, 0.26 to 1.08]; P = .08) as barriers. Respondents from centers without PRO infrastructure were more likely to perceive that not having access to a local PRO expert (aOR, 6.59 [95% CI, 3.81 to 11.42]; P < .001), being unsure how to apply PROs in clinical decisions (aOR, 4.20 [95% CI, 2.32 to 7.63]; P < .001), and being unsure about selecting PRO measures (aOR, 3.36 [95% CI, 2.00 to 5.66]; P < .001) as barriers. Heat map analyses identified the largest differences between participants from centers with and without PRO infrastructure in agreed-upon barriers were (1) not having a local PRO expert, (2) being unsure about selecting PRO measures, and (3) not recognizing the role of PROs at the institutional level. CONCLUSION Perceived barriers toward PRO implementation differ between administrators and nonadministrators and practitioners at centers with and without PRO infrastructure. PRO implementation teams should consider as part of a comprehensive strategy including frontline clinicians and administrators and members with PRO experience within teams.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.489
Teacher spread0.390 · 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