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Record W2337713735 · doi:10.1177/0003489416644619

The Development of a Tracheostomy-Specific Quality of Life Questionnaire

2016· article· en· W2337713735 on OpenAlexaff
Kristine A. Smith, J. Douglas Bosch, Guy Pelletier, Marianne MacKenzie, Monica Hoy

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2016
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsQuality of life (healthcare)Quality (philosophy)Environmental sciencePsychologyMedicineNursingPhilosophy

Abstract

fetched live from OpenAlex

OBJECTIVE: A long-term tracheostomy can be a life-altering event and can have significant effects on patients' quality of life (QOL). There is currently no instrument available to evaluate tracheostomy-specific QOL. To address this deficiency, the objective of this study was to create and preliminarily validate a pilot tracheostomy-specific QOL questionnaire to assess its feasibility. METHODS: A multidisciplinary team developed the pilot tracheostomy-specific QOL questionnaire (TQOL) in 3 phases: item generation, item review, and scale construction. The survey was administered at 0 and 2 weeks to a pilot group of tracheostomy patients with concurrent administration of a validated general QOL questionnaire at week 0. Convergence validity, test-retest reliability, and internal consistency were the primary outcome measures. RESULTS: A total of 37 patients completed the study (mean tracheostomy duration = 90 weeks). The convergence validity of the TQOL was moderately strong (r = 0.72), and the test-retest reliability was strong (r = 0.75). The TQOL also demonstrated good internal consistency (Cronbach's alpha = 0.82). CONCLUSION: The TQOL has moderately strong internal consistency, convergence validity, and test-retest reliability. While additional refinement and validation may improve the questionnaire, these initial results are promising and support further development of this tool.

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)0.001
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 score0.255
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.350
Teacher spread0.263 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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