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Record W2070593958 · doi:10.12968/ijpn.2009.15.5.42349

Survey of educators’ end-of-life care learning needs in a Canadian baccalaureate nursing programme

2009· article· en· W2070593958 on OpenAlexaffabout
Susan Brajtman, Frances Fothergill‐Bourbonnais, Valerie Fiset, Diane Alain

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsLa Cité CollégialeAlgonquin CollegeUniversity of Ottawa
Fundersnot available
KeywordsNursingContext (archaeology)End-of-life careExperiential learningNurse educationPalliative careMedical educationMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

AIM: To examine the potential need for faculty development in end-of-life care (EOLC) of theory and clinical educators in a collaborative bilingual undergraduate nursing programme in a Canadian university. METHOD: A purposive sample of 53 Anglophone and Francophone theory and clinical educators completed the Palliative Care Quiz for Nursing, the Frommelt Attitude Toward Care of the Dying Scale and an adapted Educators Educational Needs Questionnaire (Patterson et al, 1997). RESULTS: Results indicated that educators held positive attitudes towards caring for dying patients and had modest knowledge levels. Participants identified personal educational needs, preferred learning formats, support and barriers to teaching EOLC and to their participation in continuing educational programmes. Strategies to enhance the teaching and learning of EOLC content in the theory and clinical context were suggested. CONCLUSION: Nurse educators require time, opportunities and relevant resources to develop the competencies required to support the theoretical and experiential learning of students in EOLC. Recommendations include a variety of approaches for faculty development initiatives, including face to face and virtual, which allow nurse educators to share expertise.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.108
GPT teacher head0.442
Teacher spread0.334 · 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 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

Citations27
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Palliative NursingSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207