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Record W2788198759 · doi:10.1177/1476750318761534

‘Testing it in the real world’: Using action research to apply a conceptual framework to social care planning

2018· article· en· W2788198759 on OpenAlexfundno aff
Susan Collings, Angela Dew, Leanne Dowse

Bibliographic record

VenueAction Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsReflexivityPraxisTransformative learningAction researchSociologyAction (physics)Process (computing)Practice theoryConceptual frameworkResource (disambiguation)Engineering ethicsPublic relationsKnowledge managementManagement scienceEpistemologySocial sciencePedagogyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper outlines an action research methodology used to create a practice-informed resource for social care in Australia. Practitioners and researchers worked together to develop, test and refine a process to engage people with cognitive disability and complex support needs in person-centred planning. The planning approach, which calls for planners to reflect on their own skills and attitudes as well as the unique needs of an individual, has helped to improve practice in a number of fields and locations in Australia. The process marks a substantive practice shift towards recognition of planning as fundamentally relational in nature. This paper reflects on the process of action research which we describe as similarly relational and potentially transformative of the relations between researchers and practitioners. Working within a knowledge translation paradigm we show how reflexivity within the researcher/practitioner relationship in action research calls for a substantive shift in perspective by researchers to effectively work within the complex contexts of practitioners themselves. In taking this opportunity in our research practice, we identify the potential for a fundamentally different praxis to emerge, one more deeply grounded in the conceptual, political and practical relations between researchers, practitioners and those whose lives they seek to enhance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.155
metaresearch head score (Gemma)0.101
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.155
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1550.101
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.005
Science and technology studies0.0120.130
Scholarly communication0.0230.026
Open science0.0080.020
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.849
GPT teacher head0.690
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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