Using qualitative synthesis to develop a metatheory that explains how patients manage complete tooth loss
Bibliographic record
Abstract
Complete tooth loss is a leading cause of oral disability, and is among the most difficult treatment challenges in dentistry. Tooth loss, like the loss of other body parts, can generate profound emotional and social responses, but there is no comprehensive theory to explain how people psychologically manage loss of a part of their body. Given the complexity of the phenomena surrounding these issues, the purpose of this dissertation was to conduct a systematic review of the literature to search for and synthesize the psychosocial theories commonly related to the loss of anatomical parts, such as breasts, limb, or teeth, and to explain the beliefs and behaviours of people responding to such losses. The methodological challenges encountered when conducting the systematic search and qualitative synthesis of theories are also presented with proposed solutions and considerations to overcome such challenges. Finally, I explore how theories from this qualitative synthesis explain the beliefs, concerns, and responses of people who experience complete tooth loss. The findings of the search yielded 2,540 citations that referenced 288 articles referring to 89 primary theories containing 586 constructs. Through the synthesis of seven construct categories, a metatheory with essential contributions from theories related to communications, developmental regulation, emotions, resources, and social cognition was integrated to explain responses to loss. Different approaches of searching were necessary, for example, both electronic and manual searches were used, including searching of the reference list of selected articles to better understand the sources of relevant theories. Inclusion criteria were refined using iterative and inductive processes to ensure the inclusion of all relevant literature. The qualitative synthesis presented in this study was a useful approach for developing a metatheory that provided a conceptual foundation, which was used to explain how people manage the loss of anatomical parts. A metatheory synthesized 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 it expands the scope of information needed to help people 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 | 0.207 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".