Two approaches to longitudinal qualitative analyses in rehabilitation and disability research
Bibliographic record
Abstract
Purpose: Although relatively unknown within the field of rehabilitation, qualitative longitudinal research is ideal for rehabilitation and disability research that aims to understand health-related challenges over time. We describe the strengths and challenges of longitudinal qualitative research using two concrete examples.Materials and methods: Qualitative longitudinal research often involves in-depth interviews of participants on multiple occasions over time. Analytic approaches are complex, summarizing data both cross-sectionally and longitudinally. We present two detailed analytic approaches used in research with people living with HIV in Zambia and Canada.Results: Our experiences provide three recommendations. First, development of the initial analytic coding framework should include both inductive and deductive approaches. Second, given the large quantity of data generated through longitudinal qualitative research, it is important to proactively develop strategies for data analysis and management. Third, as retention of participants is challenging over time, we recommend the use of a consistent interviewer over the duration of the study to promote a trusting relationship.Conclusions: Longitudinal qualitative research has much to offer researchers and can provide clinicians with insights on the challenges of living with chronic and episodic disability. The flexibility in analytic approaches allows for diverse strategies to best address the rehabilitation and disability research questions and allow for insights into living with disability over time.
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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 | 0.344 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".