A metatheory explaining how patients manage tooth loss
Bibliographic record
Abstract
OBJECTIVES: To explore how a metatheory composed of five dominant psychosocial theories of communication, developmental regulation, emotions, resources and social cognition, explains the beliefs, concerns and experiences, of people experiencing tooth loss. BACKGROUND: Complete tooth loss is the leading cause of disability associated with oral conditions in 19 of 21 global regions, and it is among the most difficult treatment challenges in dentistry. METHODS: Interviews with 18 participants were analysed inductively using interpretive description and qualitative synthesis to explain their beliefs and experiences relating to tooth loss. RESULTS: Theoretical constructs from the five dominant theories constituting the metatheory explained the beliefs, concerns and experiences of the participants. For example, both before and after tooth loss they engaged in adaptive behaviours according to developmental regulation theory; implemented management strategies according to emotion theory, social cognitive theory, and resource theory; sought help from friends and dental professionals according to communication theory; and modified their physical and social activities according to social cognitive theory. CONCLUSION: A metatheory synthesised from five dominant theories addressing communication, personal background, emotions, resources and social awareness, offers a comprehensive and plausible explanation of how people respond psychologically and socially to the loss of their teeth, and expands the scope of information needed to help manage their loss and subsequent treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".