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Canadian cancer nurses' views on recruitment and retention

2009· article· en· W2131627603 on OpenAlexaffabout
Debra Bakker, Lorna Butler, Margaret I. Fitch, Esther Green, Kärin Olson, Greta G. Cummings

Bibliographic record

VenueJournal of Nursing Management · 2009
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsCancer Care OntarioUniversity of AlbertaUniversity of SaskatchewanLaurentian University
Fundersnot available
KeywordsCancerNursing managementNursingPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

AIM: The purpose of this study was to explore oncology nurses' perceptions about recruitment and retention. BACKGROUND: Competition among healthcare organizations to recruit and retain qualified nurses is a real-life challenge. Focusing attention on human resource planning in oncology is highlighted by both the worsening nursing shortage and cancer incidence. METHODS: A participatory action research approach was used and 12 focus groups with 91 cancer nurses were conducted across Canada to collect data about strategies that could improve recruitment and retention. RESULTS: Four themes emerged reflecting oncology nurses' beliefs and values about organizational practices that attract and retain nurses and they are as follows: (1) recognizing oncology as a specialty, (2) tacit knowledge no longer enough, (3) gratification as a retaining factor, and (4) relationship dependent on environment. CONCLUSIONS: Participants highlighted leadership, recognition and professional and continuing education opportunities as critical to job satisfaction and organizational commitment. IMPLICATIONS FOR NURSING MANAGEMENT: Recruitment and retention were viewed as a continuum where organizational investment begins with a well-developed orientation and ongoing mentorship to ensure knowledge development. The challenge for nurse leaders is to use the evidence generated from this study and previous studies to develop professional practice environments that facilitate the cultural changes needed to build and sustain a quality nursing workforce.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.101
GPT teacher head0.392
Teacher spread0.291 · 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 designNot applicable
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

Citations24
Published2009
Admission routes2
Has abstractyes

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