MétaCan
Menu
Back to cohort
Record W3210464004 · doi:10.5737/23688076314367375

Oncology clinic nurses’ attitudes and perceptions regarding implementation of routine fall assessment and fall risk screening: A survey study

2021· article· en· W3210464004 on OpenAlexafffundvenue
Schroder Sattar, Kristen R. Haase, Koen Milisen, Diane Campbell, Soo Jung Kim, Haji Chalchal, Cindy Kenis

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsSaskatchewan Cancer AgencyUniversity of SaskatchewanUniversity of British Columbia
FundersNational Cancer InstituteCollege of Nursing, University of Saskatchewan
KeywordsMedicineThematic analysisDescriptive statisticsFamily medicineFall preventionPopulationOutpatient clinicNursingHuman factors and ergonomicsPoison controlMedical emergencyQualitative researchInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Falls in older adults with cancer are often under-recognized and under-reported. The objective of this study was to explore oncology clinic nurses' willingness and perceived barriers to implement routine falls assessment and falls screening in their practice. Nurses working in outpatient oncology clinics were invited to complete an online survey. Data were analyzed using descriptive statistics and sorted into thematic categories. The majority of respondents indicated willingness to routinely ask older patients about falls (85.7%) and screen for fall risks (73.5%). The main reasons for unwillingness included: belief that patients report falls on their own, lack of time, and lack of support staff. Findings from this study show many oncology nurses believe in the importance of routine fall assessment and screening and are willing to implement them routinely, although falls are not routinely asked about or assessed. Future work should explore strategies to address barriers nurses face given the implications of falls amongst this vulnerable population.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.503
Teacher spread0.428 · 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 designObservational
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

Citations6
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Oncology Nursing JournalSame topicBalance, Gait, and Falls PreventionFrench-language works237,207