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Exploring the Transition of Health Workers from Students to Professionals

2022· article· en· W4400931548 on OpenAlexaff
Sandra P. Hirst, Rebecca Stares, Mohamed Toufic El Hussein

Bibliographic record

VenuePapers on postsecondary learning and teaching. · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsTransition (genetics)Health professionalsPsychologyPolitical scienceHealth careChemistry

Abstract

fetched live from OpenAlex

Numerous studies have documented the transition difficulties that health care students experience when they move into paid professional practice. Addressed in this paper are three questions that challenge post-secondary educators. (1) What might a successful transition look like for a “new” health care professional? (2) What individual actions could we, as educators, initiate to promote successful transitions for our students? Should we accept mentoring as an effective strategy to support students’ transition, and if so (3) What might demonstrate effective mentorship? The evidence suggests that the process of transition into professional life for health care workers may be facilitated when post-secondary education experiences include the development of strong professional networks, the provision of supportive learning opportunities, supports for developing self-confidence in their new roles are provided and mentoring continues after graduation.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.409
Teacher spread0.338 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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