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Record W2889464861 · doi:10.1002/pra2.2017.14505401101

Information world mapping to explicate the information‐care relationship in dementia care

2017· article· en· W2889464861 on OpenAlexaff
Nicole Dalmer

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWestern University
Fundersnot available
KeywordsWork (physics)EthnographyDementiaInformation systemKey (lock)Information needsKnowledge managementSociologyComputer sciencePsychologyWorld Wide WebMedicinePolitical scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

ABSTRACT Information world mapping is a helpful data elicitation technique to make visible the hidden work of finding, using and making sense of information. This methods‐based paper explores the utility of a mapping exercise both within an institutional ethnographic study and in eliciting informants' understandings and descriptions of their care‐related information work. Eleven family caregivers of community‐dwelling older adults living with dementia drew maps of their information worlds. Each map depicts a unique combination of information resources (people, agencies, texts and websites) accessed, relationships that shape the information work in addition to key locations frequented to access information. Given the difficulty in delineating the boundaries of information, the mapping exercise served as a helpful tool for caregivers to make visible the intricacies of their information work, including the barriers encountered and inventive strategies created to access, use and translate information needed to guide and support their care work.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.007
Open science0.0010.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2017
Admission routes1
Has abstractyes

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Same venueProceedings of the Association for Information Science and TechnologySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207