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Record W1986516413 · doi:10.5596/c05-007

Developing information literacy skills in nursing and rehabilitation therapy students

2005· article· en· W1986516413 on OpenAlexvenueaboutno aff
Paola Durando, Patricia Oakley

Bibliographic record

VenueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumEntry LevelRehabilitationInformation literacyMedical educationOccupational therapyNursingPsychologyHealth careCritical appraisalMedicinePedagogyAlternative medicinePhysical therapy

Abstract

fetched live from OpenAlex

The environment in which nurses and rehabilitation therapists practice is rapidly evolving, resulting in changes in the skill sets and competencies required of new graduates. Evidence-based practice models, for example, require that entry-level nurses, physical therapists, and occupational therapists have the ability to identify, locate, and critically appraise research findings. This paper will describe curriculum-integrated, for-credit information literacy programs developed by the authors in collaboration with faculty members from the Schools of Nursing and Rehabilitation Therapy at Queen's University in Kingston, Ontario. The short-term goal of these programs is to teach undergraduate and graduate students advanced search strategy skills and critical appraisal techniques that will enable them to explore the implications of their literature findings. The long-term goal is to graduate practitioners who not only will have the skills to practice evidence-based health care but also will participate in scholarly activities and thus contribute to the evidence base in their disciplines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.384
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2005
Admission routes2
Has abstractyes

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Same venueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du CanadaSame topicHealth Sciences Research and EducationFrench-language works237,207