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Record W4367049165 · doi:10.1186/s13012-023-01265-4

Implementation of electronic prospective surveillance models in cancer care: a scoping review

2023· review· en· W4367049165 on OpenAlex
Christian Lopez, Kylie Teggart, Mohammed Ahmed, Anita Borhani, Jeffrey Kong, Rouhi Fazelzad, David M. Langelier, Kristin L. Campbell, Tony Reiman, Jonathan Greenland, Jennifer M. Jones, Sarah Neil‐Sztramko

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueImplementation Science · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of NewfoundlandSaint John Regional HospitalPrincess Margaret Cancer CentreMcMaster UniversityUniversity Health NetworkUniversity of TorontoUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionMedicineImplementation researchHealth informaticsHealth services researchKnowledge translationIntervention (counseling)Process managementRehabilitationHealth administrationQuality managementMedical educationNursingFamily medicinePublic healthKnowledge managementComputer scienceBusinessPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Electronic prospective surveillance models (ePSMs) for cancer rehabilitation include routine monitoring of the development of treatment toxicities and impairments via electronic patient-reported outcomes. Implementing ePSMs to address the knowledge-to-practice gap between the high incidence of impairments and low uptake of rehabilitation services is a top priority in cancer care. METHODS: We conducted a scoping review to understand the state of the evidence concerning the implementation of ePSMs in oncology. Seven electronic databases were searched from inception to February 2021. All articles were screened and extracted by two independent reviewers. Data regarding the implementation strategies, outcomes, and determinants were extracted. The Expert Recommendations for Implementing Change taxonomy and the implementation outcomes taxonomy guided the synthesis of the implementation strategies and outcomes, respectively. The Consolidated Framework for Implementation Research guided the synthesis of determinants based on five domains (intervention characteristics, individual characteristics, inner setting, outer setting, and process). RESULTS: Of the 5122 records identified, 46 interventions met inclusion criteria. The common implementation strategies employed were "conduct educational meetings," "distribute educational materials," "change record systems," and "intervene with patients to enhance uptake and adherence." Feasibility and acceptability were the prominent outcomes used to assess implementation. The complexity, relative advantage, design quality, and packaging were major implementation determinants at the intervention level. Knowledge was key at the individual level. At the inner setting level, major determinants were the implementation climate and readiness for implementation. At the outer setting level, meeting the needs of patients was the primary determinant. Engaging various stakeholders was key at the process level. CONCLUSIONS: This review provides a comprehensive summary of what is known concerning the implementation of ePSMs. The results can inform future implementation and evaluation of ePSMs, including planning for key determinants, selecting implementation strategies, and considering outcomes alongside local contextual factors to guide the implementation process.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.653
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.009
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.783
GPT teacher head0.793
Teacher spread0.010 · 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