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Record W4285798875 · doi:10.5737/23688076323381

Perceptions of healthcare professionals regarding home-based pediatric cancer care provided in French: A qualitative descriptive study

2022· article· en· W4285798875 on OpenAlexafffundvenueabout
Julie Chartrand, Lindsay Jibb, Camille Grandmont, Élisabeth Hardy-Bélanger, Sara Y. Cheng, Rebecca Balasa, Donna L. Johnston

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of TorontoUniversity of Ottawa
FundersPediatric Oncology Group of Ontario
KeywordsThematic analysisHealth carePerceptionDescriptive researchNursingQualitative researchHealth professionalsPediatric hospitalPediatric cancerHealthcare deliveryMedicineDescriptive statisticsFamily medicinePsychologyCancerPediatricsSociologyPolitical science

Abstract

fetched live from OpenAlex

Goal: This study aims to explore how healthcare professionals perceive home-based pediatric cancer care provided in French. Methodology: A qualitative descriptive study was conducted using semi-directed individual interviews of 22 healthcare professionals. A thematic analysis of the transcribed interviews was carried out independently by two members of the research team. Findings: Pediatric cancer care is readily available in French in Quebec, but access to French-language services in Ontario is limited. The possible causes and effects of this lack of access and potential solutions are discussed in this paper. Conclusion: The perceptions compiled in this study should be taken into account to help provide quality home-based pediatric cancer care in French.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.063
GPT teacher head0.435
Teacher spread0.372 · 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 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

Citations2
Published2022
Admission routes4
Has abstractyes

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