THE ORAL HEALTH IMPACTS OF CANCER AND ITS TREATMENT ON CHILDREN’S WELL-BEING: A QUALITATIVE PROTOCOL
Bibliographic record
Abstract
Introduction While cancer treatments clearly improve cancer outcomes, they inadvertently damage healthy tissues and organs, including causing oral and dental complications. Oral health complications can lead to cognitive, psychological, and social impairments which can have a profound impact on children’s well-being. Current knowledge of these consequences comes from clinical studies focusing on caregivers' viewpoints. Though it is important to directly involve children in research to gain a firsthand understanding of their experiences. Objectives The aim of this study is to better understand how oral health effects of cancer and its treatment impact the well-being of children with, or who have survived, cancer from their own perspective. Methodology Participants will be recruited from a tertiary care pediatric hospital. We will use a participatory hermeneutic ethnographic methodology and a Childhood Ethics theoretical framework to center children’s perspectives. Data will be generated through document analysis, participant observation and semi-structured interviews. We will observe discussions among children, families, and healthcare professionals, and conduct interviews with stakeholders: children (n=10-15) who are undergoing or completed treatments; parents/caregivers (n=10-15); and healthcare professionals (n=8-10). Interviews will be audio-recorded and transcribed. Analysis will use SAMSSA methods to identify key aspects of children’s experiences related to their oral health and the impact it has on their well-being, as well as health and social factors that affect this experience. Significance: Through this study, we will gain insight into how cancer and treatment-induced oral health complications impact children’s well-being, results from which will be translated into clinical and policy recommendations
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".