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Record W2790519108 · doi:10.1111/cdoe.12363

Anxiety and anger of homeless people coping with dental care

2018· article· en· W2790519108 on OpenAlexaffabout
Anjali Mago, Michael I. MacEntee, Mario Brondani, James Frankish

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAngerCoping (psychology)AnxietyIndigenousDental careHealth careNursingFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To reveal and describe from open-ended interviews how homeless people in Vancouver interpret, appraise and cope with dental care. METHODS: Audio-recorded interviews with 25 homeless people (18 men and 7 women; age range: 25-64 years), purposefully selected for a range of experiences, were transcribed and analysed inductively. The process of interpretive description drawing from the Behavioral Model for Vulnerable Populations and Lazarus's Theory of Emotions identified how participants appraised and coped with dental care. RESULTS: Four dominant themes emerged: barriers to care; service use; opinions on dental health; and improving dental services. Participants were anxious about the cost of dentistry and fearful of dentists. They got emergency dental care with difficulty, usually in hospital emergency departments although mostly they preferred self-treatment. They acknowledged the importance of dental health but felt stigmatized by their homelessness and visibly unhealthy mouths. They wanted accessible dental services with financial assistance from government, more widespread information about community dental clinics, and, notably among the Indigenous participants, less humiliating discrimination from dentists. CONCLUSIONS: Homeless people have difficulty coping with dental care. They believe that dentistry is frightening, humiliating and expensive, and governments are neither sympathetic to their disability nor willing to provide helpful information about community dental clinics or sufficient dental benefits for their needs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.092
GPT teacher head0.441
Teacher spread0.350 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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