The processes of engagement in information-seeking behavior for individuals with diabetes who developed diabetic foot ulcer: A constructivist grounded theory study
Bibliographic record
Abstract
To describe the process of engagement in information seeking behavior for individuals with type 1 and type 2 diabetes. Methodology: Constructivist grounded theory. The data was gathered through thirty semi-structured interviews of participants attending a wound care clinic in Southeast, Ontario, Canada. The waiting period taken to seek appropriate help varied from weeks to months. Results: "The processes of engagement in information-seeking behavior about diabetes" are organized as follows: 1) discovering diabetes, 2) reactions to the diagnosis, and 3) engaging in self-directed learning. For most participants, the diagnosis of diabetes was unexpected and usually confirmed after a long period of experiencing a diversity of symptoms. The terms used mostly by participants were "I started to wonder" and "Something was wrong with me." After being diagnosed with diabetes, participants sought information to learn about it. Most of them engaged in self-directed learning to acquire knowledge about their illness. Conclusion: Although the Internet is often used to seek information, healthcare providers and support network also played an important role in supporting participants information-seeking behavior learn about diabetes. The unique needs of people with diabetes must be taken into consideration during their diabetes care journey. These findings call for the need to provide education about diabetes from the time they are diagnosed and direct them to reliable resources of information.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".