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Record W2074503425 · doi:10.12927/hcpol.2014.23810

Waiting for a Specialist Consultation for a New Condition in Ontario: Impacts on Patients' Lives

2014· article· en· W2074503425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealthcare policy · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of TorontoQueen's University
Fundersnot available
KeywordsWorryAnxietyImmigrationMedicineFamily medicinePsychologyPsychiatryNursingGeography

Abstract

fetched live from OpenAlex

As leading barriers to specialist care, wait times are at the forefront of the Canadian healthcare policy agenda. However, knowledge is limited about how wait times affect patients' lives. We utilized the 2010 Canadian Community Health Survey to examine the experience of patients requiring a consultation with a medical specialist for a new condition. Multivariate logistic regression predicted the likelihood that a respondent self-reported his or her life was affected. Subsequent cross-tabulations determined the ways in which life was affected. Females, middle-aged respondents, new immigrants and those with low income and poor health status were more likely to report their life was affected. Worry, stress and anxiety were the most frequently reported impacts, followed by pain, stress on family/friends, deterioration of health and loss of work. Our research demonstrates a need to address the impacts of wait times on health and well-being, with a focus on particular subpopulation groups.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.315
Teacher spread0.278 · 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