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Record W2994881993

More than Just Pulling Teeth: The Impact of Dental Care on Patients' Lived Experiences.

2018· article· en· W2994881993 on OpenAlexaboutno aff
Loretta Kerr

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialOutreachEmployabilityDental careQualitative researchPsychologyMedicineNursingDentistryPsychiatrySociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore the psychosocial impacts of dentistry as described by patients. METHODS: I conducted 6 qualitative interviews with people whose access to dental care had been limited because of financial barriers, but who had recently undergone significant treatment through a dental outreach program. RESULTS: In addition to physical benefits (including improved sleep and diet), participants discussed how dental treatment led to a greater level of confidence and an improved self-concept. They provided powerful examples of how this confidence boost improved their social interactions and relationships. They also felt more confident about their employability. CONCLUSION: The benefits of adequate dental care extend well beyond its physical and medical aspects. Dental health is connected inextricably with people's sense of self and social functioning. The far-reaching consequences of dentistry explored in this research raise questions about the inequalities in Canada's current system and the need to address them.

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)0.004
metaresearch head score (Gemma)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.495
Teacher spread0.358 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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