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Record W2066073429 · doi:10.1177/1742395313516133

Identifying gaps in knowledge: A map of the qualitative literature concerning life with a neurological condition

2014· review· en· W2066073429 on OpenAlexaff
Åsa Audulv, Tanya Packer, Joan Versnel

Bibliographic record

VenueChronic Illness · 2014
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsQualitative researchMedicinePsychologyData scienceNeuroscienceComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe patterns in the qualitative literature regarding the everyday experience of living with a neurological condition; to identify areas of depth as well as gaps in the existing knowledge base. METHODS: An extensive search of the literature yielded 474 articles meeting the inclusion criteria. Data extraction, based on scrutiny of both abstract and full text article included country of origin, diagnosis, stated aim, methodological framework/design, participants, and data collection method(s). Studies were categorized into 27 topics within four broad foci. RESULTS: Four broad foci describe the field: impact and management, daily activities and occupations, impact on family, and the healthcare experience. Overall the research is unevenly distributed by diagnosis; some are well represented while others are the subject of little research. Even diagnoses well represented in quantity can be limited in breadth. DISCUSSION: Possible explanations for the patterns of emphasis include: a focus on issues and problems, highlighted points of contact between patients and healthcare providers, and ability of participants to voice their views. The literature is also characterized by limited across diagnoses research or that comparing the experience of people with different diagnoses. There is a need for more research in particular diagnoses; more varied data collection methods and acknowledgement of ethnicity, gender, discrimination, and social inequalities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.213
GPT teacher head0.577
Teacher spread0.364 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2014
Admission routes1
Has abstractyes

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