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Record W3011184403 · doi:10.1016/j.jjodo.2020.100014

A qualitative synthesis of theories on psychosocial response to loss of breasts, limbs or teeth

2020· review· en· W3011184403 on OpenAlexaff
Maha M. Al‐Sahan, Michael I. MacEntee, Sally Thorne, S. Ross Bryant

Bibliographic record

VenueJournal of Dentistry · 2020
Typereview
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialMedicineQualitative researchDentistryPsychologyOrthodonticsPsychotherapistSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this systematic review is to generate a qualitative synthesis of psychosocial theories being used to explain the beliefs and behaviors of people responding to loss of anatomical parts, such as breasts, limbs, or teeth. DATA & SOURCES: A search in four databases and subsequent manual search of pertinent reference lists identified theories on how people respond to loss of anatomical parts. Findings were analyzed by consensus through a three-stage interpretive process to: deconstruct and interpret each theory, categorize similar theoretical constructs, and distill the dominant theoretical perspectives identified as most relevant to explaining responses to the loss. STUDY SELECTION: 2540 citations produced 288 articles referring to 89 primary theories containing 586 constructs. Through synthesis of seven construct categories a metatheory with essential contributions from theories on communications, developmental regulation, emotions, resources, and social cognition can explain responses to loss. CONCLUSIONS: This qualitative synthesis provides a conceptual foundation for further investigations to explain how people manage loss of anatomical parts. CLINICAL SIGNIFICANCE: The combination of five dominant theories serves as a prelude to the development of a metatheory, which will further help determine how people psychosocially respond to the loss of anatomical parts.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.061
GPT teacher head0.433
Teacher spread0.372 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2020
Admission routes1
Has abstractyes

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