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Record W2057906438 · doi:10.1108/17549451211214373

The search for a suitable outcome measure for use in evaluating the outcome of provision of an environmental control system

2012· article· en· W2057906438 on OpenAlexaboutno aff
Phil Palmer, Jill Jepson

Bibliographic record

VenueJournal of Assistive Technologies · 2012
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)OriginalityControl (management)Scale (ratio)Service (business)PsychologyService providerMeasure (data warehouse)Medical educationKnowledge managementApplied psychologyProcess managementOperations managementComputer scienceMedicineEngineeringBusinessMarketingSocial psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to report on the journey, by the Access to Communication and Technology (ACT) Service, towards a suitable measure for use in evaluating the outcome of provision of an environmental control (EC) system. Design/methodology/approach This journey has involved various approaches and methodologies. A literature search together with qualitative research, by the first author, demonstrated that the power of EC provision lies in the psycho‐social domain. Subsequently, ACT evaluated the 26‐item Psycho‐social Impact of Assistive Devices Scale (PIADS), as a research project. This was deemed to be not fit for the purpose of outcome measure in routine clinical practice. During the course of this ACT research project, a shortened version of PIADS (the PIADS‐10) was developed at the University of Western Ontario. Findings ACT has concluded that the PIADS‐10 is more likely to be fit for purpose, as it is shorter, more understandable for the patient, and easier for the clinician to administer. Originality/value Service providers and commissioners should consider PIADS‐10 as a means to evaluate outcome in EC.

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)0.013
metaresearch head score (Gemma)0.007
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 score0.061
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.254
GPT teacher head0.497
Teacher spread0.243 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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