Feasibility and Barriers of an Olfactory Training Intervention
Bibliographic record
Abstract
Abstract Olfactory dysfunction is a common issue in late-life and can be an early indicator for neurodegenerative diseases. Further, olfactory interventions not only improve olfaction but have shown promise for the delay and treatment of dementia. This study aimed to better understand the feasibility and barriers of implementing an olfactory intervention. Participants (N=23) between the ages of 52-86 (mean=71) years were recruited from the community. A demographic questionnaire showed participants were all non-smokers and identified as women (70%), men (26%), and transgender (4%). The majority were married (61%), while some were separated or divorced (17%), widowed (13%), or single (9%). Four focus groups, guided by both structured and open-ended questions, were conducted and audio-recorded with 3-7 unique participants per group. Data were transcribed, thematically analyzed, and independently coded, which resulted in three overarching themes: (1) cognitive, genetic, and environmental factors of smell, (2) methods to reduce barriers and increase the feasibility of an intervention, and (3) flexibility with technology use. Findings suggest that implementing an olfactory intervention is feasible and of interest to older populations especially when provided with detailed training protocols that have flexibility in the amount of technology used within the study. Barriers included sensitivity to smells, allergies, and dexterity issues. Reducing these barriers will facilitate implementation and decrease the likelihood of attrition. Consulting the target population provides insights into barriers, participant interest, and can assist with the development of training and intervention programs.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".