What Does it Mean to Live with Thalassemia? An Interpretative Phenomenological Inquiry
Bibliographic record
Abstract
Living with Thalassemia, means that the body is unable to produce normal levels of hemoglobin to carry oxygen throughout the body. Without sufficient levels of hemoglobin (due to inefficiency of bone-marrow to produce normal red blood cells), one can experience signs and symptoms, such as severe anemia, chronic fatigue and other serious health concerns. My interest in this topic is because, I live with this condition, but, also interested in the lived experience of the Thalassemia community. My research is a phenomenological exploration through interpretation of research participants’ narratives. The overarching goal of the proposed research is to investigate the contribution to the medical personnel who may use the findings from the study to improve the clinical care from not only from patient centered, but also from a whole person care perspective. There are various clinical and psycho-social challenges such as, academics, career, and family / friend relationship issues. Families address the treatment issues of Thalassemia on a continuum Extreme Drive - No Drive to improve their lives. Thalassemia was seen as a “fatal condition,” today it is a “manageable condition,” therefore, we need to learn, how best to thrive to lead a healthy lifestyle? Through this research I am hoping to share my story and with others to inspire people living with Thalassemia, to go beyond managing towards thriving.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".