Mad Dog Nutrition Program Consult: A Qualitative approach to Perceptions of Task and Barrier Self-Efficacy, and Long-term Adherence
Bibliographic record
Abstract
Past research has demonstrated that neurological populations (i.e., spinal cord injury and multiple sclerosis) can benefit from an anti-inflammatory diet (Allison & Ditor, 2015; Allison et al., 2016). Although able to reduce inflammation and improve other health related measures, adherence to the diet was substantially lower one year later (Allison & Ditor, 2018). Investigation into factors impacting adherence was successful in identifying applicable barriers and facilitators (Bailey et al., 2017). Using existing knowledge, a pilot nutrition consult (Mad Dog Consult) was created in hopes of improving adherence to the diet. After the consult and a 1-month intervention period, participants were later interviewed with regard to their experience. Interviews were conducted under a constructivist world view and analysis was guided by reflexive thematic analysis (RTA). This study aimed to 1) determine how the Mad Dog Consultation changed individual perceptions of task and barrier self-efficacy (SE) and long-term adherence, and 2) gather participant feedback regarding effectiveness and delivery of the consultation for future modifications. Resulting themes are as follows. Primary themes: Independent Investigation* and Utilizing Self-Awareness; secondary themes: Learning & Trying New Things, Resources & Tools*, Confidence & Commitment, Support & Cooperation*, Health Concerns & Considerations, Inadequate/Ineffective knowledge*, Emotion & Environment, Sourcing & Expense, Accessibility (Physical Challenges), and Inconvenience; and sub-themes: Small Changes, Weight Loss, and Energy Level & Focus. While these findings confirm the consult’s positive impact on addressed barriers and facilitators, it also indicates the need for booster sessions, further adaptation to “busy lives”, and reinforcing of individual strengths. Therefore, it can be concluded that while the consult was demonstrated to act as a catalyst for healthy change, further investigation is still required.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".