Blog Posting After Lung Cancer Notification: Content Analysis of Blogs Written by Patients or Their Families
Bibliographic record
Abstract
BACKGROUND: The advent and spread of the Internet has changed the way societies communicate. A portion of information on the Internet may constitute an important source of information concerning the experiences and thoughts of patients and their families. Patients and their families use blogs to obtain updated information, search for alternative treatments, facilitate communication with other patients, and receive emotional support. However, much of this information has yet to be actively utilized by health care professionals. OBJECTIVE: We analyzed health-related information in blogs from Japan, focusing on the feelings and satisfaction levels of lung cancer patients or their family members after being notified of their disease. METHODS: We collected 100 blogs written in Japanese by patients (or their families) who had been diagnosed with lung cancer by a physician. These 100 blogs posts were searchable between June 1 and June 30, 2013. We focused on blog posts that addressed the lung cancer notification event. We analyzed the data using two different approaches (Analysis A and Analysis B). Analysis A was blog content analysis in which we analyzed the content addressing the disease notification event in each blog. Analysis B was patient's dissatisfaction and anxiety analysis. Detailed blog content regarding patient's dissatisfaction and anxiety at the individual sentence level was coded and analyzed. RESULTS: The 100 blog posts were written by 48 men, 46 women, and 6 persons whose sex was undisclosed. The average age of the blog authors was 52.4 years. With regard to cancer staging, there were 5 patients at Stage I, 3 patients at Stage II, 14 patients at Stage III, 21 patients at Stage IV, and 57 patients without a disclosed cancer stage. The results of Analysis A showed that the proportion of patients who were dissatisfied with the level of health care exceeded that of satisfied patients (22% vs 8%). From the 2499 sentences in the 100 blog posts analyzed, we identified expressions of dissatisfaction and anxiety in 495 sentences. Our results showed that there were substantially more posts concerning "Way of living, reasons for living, set of values" and "Relationships with medical staff (own hospital)" than in previous studies (Analysis B). CONCLUSIONS: This study provides insight into the feelings of dissatisfaction and anxieties held by lung cancer patients and their families, including those regarding the "Way of living, reasons for living, set of values" and "Relationship with medical staff (own hospital)," which were inaccessible in previous survey analyses. When comparing information obtained from patients' voluntary records and those from previous surveys conducted by health care institutions, it is likely that the former would be more indicative of patients' actual opinions and feelings. Therefore, it is important to utilize such records as an information resource.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".