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Record W2354911298 · doi:10.1177/1471301216638762

Micro-citizenship, dementia and long-term care

2016· article· en· W2354911298 on OpenAlexaff
Clive Baldwin, Michelle Greason

Bibliographic record

VenueDementia · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New BrunswickSt. Thomas University
Fundersnot available
KeywordsCitizenshipPersonhoodDementiaSociologyPoliticsPower (physics)Inclusion (mineral)Public relationsPsychologyPolitical scienceLawGender studiesMedicine

Abstract

fetched live from OpenAlex

In recent years there has been an increasing interest in the concept of citizenship as a lens through which to understand dementia practice. This move from an individualist, personhood-based approach towards an understanding of people with dementia as a group facing social and structural discrimination parallels, in some ways, that previously seen in the realms of disability and mental health which have sought to politicize those experiences. In so doing, the debate has sought to reconfigure power relations, insisting that members of such discriminated groups are people with power entitled to the same from life as everyone else. Much of the discussion to date has, understandably, focused on the larger issues of social inclusion, rights and responsibilities - reflecting the traditional concern of citizenship of individuals' relationship to the state or the society in which they live. More recently, there has been a move to conceptualising citizenship as a practice - something that is realised through action and in relationship - rather than a status bestowed. In this paper, we seek to contribute to the discussion by introducing the concepts of midi- and micro-citizenship, taken from organisation studies, as a further means by which to link the personal and the political, and as grounds to build citizenship-alliances between people with dementia living in long-term care (LTC) facilities and front-line dementia care staff. We will then seek to illustrate the usefulness of these concepts in understanding citizenship in practice in LTC facilities through analysis of data drawn from focus groups involving LTC staff, and interviews with family carers whose relatives live in LTC facilities. In conclusion, we will explore some of the possibilities that such an approach holds for dementia care practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.348
Teacher spread0.322 · 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 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

Citations41
Published2016
Admission routes1
Has abstractyes

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