Combining Qualitative and Quantitative Data Collection and Analysis Methods in Understanding Multiple Sclerosis Fatigue Management
Bibliographic record
Abstract
Combining qualitative inquiry with quantitative frequency data in a mixed methods research approach was valuable in understanding the strategies that people with Multiple Sclerosis (MS) used to manage their fatigue. Understanding its management is important because fatigue is often described by people with MS as their most disabling symptom and can pervade all aspects of a person's life. This research aimed to identify how often people used particular fatigue management strategies and the factors that influenced their use in the context of daily life. The methodology was Qualitative Description with phenomenological overtones using convenience sampling, interviews with semi-structured and structured components, and frequencies combined with typology development. Thirty-one people with MS from Chicago, Ill and Brisbane, Queensland in Australia who experienced fatigue were interviewed and asked to sort 15 cards with common fatigue management strategies into 3 categories, indicating whether they used them regularly, sometimes or never. Frequency counts were used to identify the number of participants who placed a specific strategy in a particular category. Frequencies were used to develop a typology with which to code the qualitative data. The strategies placed most frequently in each category were: Plan / organize the day used regularly, Ask someone for help used sometimes, and Use mobility devices never used. Mixed methods research techniques enabled us to explore the participants' fatigue management in greater depth. Each data type (i.e., qualitative and quantitative data) complemented the other and led to a richer understanding than either could provide in isolation.
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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 | 0.304 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".