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Record W2790853680 · doi:10.1111/jonm.12540

Policy to practice: Investment in transitioning new graduate nurses to the workplace

2018· article· en· W2790853680 on OpenAlexaffabout
Andrea Baumann, Mabel Hunsberger, Mary Crea‐Arsenio, Noori Akhtar‐Danesh

Bibliographic record

VenueJournal of Nursing Management · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsHealth Sciences CentreMcMaster University Medical CentreMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMentorshipGraduation (instrument)WorkforceNursingGovernment (linguistics)Nursing managementQuality (philosophy)Health careWorkforce managementPsychologyMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

AIM: To analyse nurses' perceptions of the impact of an extended transition programme on key dimensions of care delivery 1-6 years after graduation. The dimensions included decision-making, communication, care management, system integration and commitment. BACKGROUND: Health care employers in Ontario, Canada, can apply for government funding to support an extended transition programme for new graduate nurses that includes orientation and mentorship. METHODS: A cross-sectional study design was used. Nurses who participated in the transition programme were compared with nurses who did not. A survey was administered to a convenience sample of 2369 nurses. RESULTS: There were statistically significant differences between the two groups. Nurses in the transition programme had higher mean scores on the key dimensions of care delivery. Results were confirmed when controlling for length of time since graduation. CONCLUSION: Extended transition benefits new graduate nurses. It has a lasting effect over time and impacts key dimensions of care delivery. It can also enhance workforce integration and reduce turnover. IMPLICATIONS FOR NURSING MANAGEMENT: Responding to the needs of new graduate nurses has potential long-term advantages for health care organisations and can influence both quality and delivery of 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.047
GPT teacher head0.395
Teacher spread0.349 · 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
GenreCommentary

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

Citations34
Published2018
Admission routes2
Has abstractyes

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