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Record W2899189131 · doi:10.1111/ecc.12953

Perspectives of healthcare professionals on patient Internet use during the cancer experience

2018· article· en· W2899189131 on OpenAlexaffabout
Kristen R. Haase, Roanne Thomas, Wendy Gifford, Lorraine Holtslander

Bibliographic record

VenueEuropean Journal of Cancer Care · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of OttawaUniversity of Saskatchewan
Fundersnot available
KeywordsHealth professionalsThematic analysisHealth careMedicineNursingFocus groupThe InternetFamily medicineMedical educationQualitative researchSociology

Abstract

fetched live from OpenAlex

In this study, we document cancer healthcare professionals' views of patients' use of cancer-related Internet information (CRII) and their views on how it informs the ways patients interact with healthcare professionals and services from the point of view of health professionals. We used an interpretive descriptive approach, conducting interviews and focus groups with oncology healthcare professionals (n = 21) at a University-affiliated western Canadian cancer treatment centre. Data were analysed using thematic analysis. We present an initial understanding of how CRII alters, informs and modulates patients' cancer experience and relates to their interactions with healthcare professionals and services. Findings were synthesised into two thematic categories: pragmatic concerns and priorities; and processes and practices. Healthcare professionals were supportive of patients' needs for more information, particularly at key points in the cancer trajectory when information may be lacking. Participants concurred that CRII could positively benefit patients and, if shared with their healthcare professional, could benefit the patient-healthcare professional relationship. Oncology healthcare professionals provide pivotal information to patients; thus, they are well situated to engage patients in discussions about CRII and incorporate this into patient encounters. These actions may open new lines of communication with patients, strengthen the patient-professional relationship and empower patients to be engaged in their own care.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.058
GPT teacher head0.465
Teacher spread0.408 · 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.

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

Citations10
Published2018
Admission routes2
Has abstractyes

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