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Record W4405220351 · doi:10.1016/j.ssmqr.2024.100513

Living documents: A longitudinal data collection method for health services research

2024· article· en· W4405220351 on OpenAlexafffund
Madelyn daSilva, Sameth Taro Hang, Shannon L. Sibbald

Bibliographic record

VenueSSM - Qualitative Research in Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health Research
KeywordsData collectionLongitudinal dataData scienceComputer scienceWorld Wide WebSociologyData miningSocial science

Abstract

fetched live from OpenAlex

Qualitative research tools offer health sciences researchers the ability to understand complex, varied, and nuanced facets of an individual’s lived experiences. Several of these tools include observations, interviews, and focus groups, each with its own advantages and limitations. We created an alternative tool, the Living Document, an iterative, longitudinal, open-ended, and adaptable questionnaire that overcomes the barriers presented by other qualitative research tools. The Living Documents allows researchers to better understand and familiarize themselves with the research context, understand change over time, and capture the perspectives of research participants. As a proof of concept, the Living Document was employed within a chronic disease program embedded within primary care called the Best Care COPD (BCC) program to better understand its growth and implementation in new patient sites. Given the iterative and sequential nature of the tool employed within the BCC program, its compatibility with other data collection tools, and its longitudinal use, the Living Document was shown to be a valuable tool for the field of health sciences and for implementation research.

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)0.245
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2450.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.005
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 head0.707
GPT teacher head0.781
Teacher spread0.073 · 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 designQualitative
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

Citations0
Published2024
Admission routes2
Has abstractyes

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