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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 OpenAlexaff
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

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.

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)0.028
metaresearch head score (Gemma)0.076
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 score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.076
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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

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

Citations17
Published2024
Admission routes1
Has abstractyes

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