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Synthesis of qualitative research and evidence-based nursing

2007· review· en· W2169153656 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Nursing · 2007
Typereview
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsYork University
Fundersnot available
KeywordsNursingQualitative researchEvidence-based nursingPsychologyNursing researchMedicineSociologyAlternative medicine

Abstract

fetched live from OpenAlex

Evidence-based nursing is central to nursing practice. Systematic reviews have played a key part in providing evidence for decision making in nursing. Traditionally, these have consisted of syntheses of randomized controlled trials. New approaches to combining research include the synthesis of qualitative research. This article discusses the development of research synthesis as a method for creating evidence for nursing practice. It focuses on how the new approach of synthesizing qualitative research may contribute to nursing and its evidence base by examining practical examples. It concludes that qualitative synthesis may contribute to: the development of nursing theory; providing context and meaning to evidence of effectiveness identified in quantitative research; more effective use of primary data; enhancing the generalizability of qualitative research; the identification of future nursing research topics.

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.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.459
GPT teacher head0.586
Teacher spread0.127 · 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