The Journey Toward Engagement in Self-care Management of Diabetic Foot Ulcer: A Constructivist Grounded Theory Study
Bibliographic record
Abstract
Background: Worldwide, diabetic foot ulcer (DFU) is the leading cause of emergency room admissions, long hospital stays, and long-term disability for individuals living with diabetes. Of all individuals with diabetes, 15–25 percent develop a DFU at some time in their lives; and 25 percent of these foot ulcers end in lower extremity amputation (LEA). While many investigators of DFU have been focused on pharmacological and technical approaches, little attention has been given to non-pharmacological approaches such as self-care management (SCM). The purpose of this qualitative study was to develop a theory that explains the process of engagement in SCM for individuals with DFUs and to identify the contextual factors influencing their engagement. Methods: A constructivist grounded theory, informed by Charmaz, was used to guide the study design. Theoretical sampling and intensive semi-structured interviews were conducted with 30 individuals with active DFU attending a wound care clinic in southeastern Ontario, Canada, in August of 2017. Data analysis was conducted alongside data collection in a cyclic research process, which began with initial coding, and proceeded to focused coding or selective coding that resulted in a model representative of the theory. Results: The study results draw attention to the complexity that exists for individuals seeking to balance life and manage diabetes and DFU. The categories and their set of concepts comprise the theory called “The Journey Toward Engagement in SCM”. The process involved five phases: 1) perception of illness, 2) noticing foot changes, 3) realizing seriousness, 4) beginning to learn about SCM, and 5) engaging in SCM. The process is influenced by multiple contextual factors, including aging and/or living with multiple chronic conditions, motivations, self-efficacy, SCM knowledge, SCM support, and access to services and resources. Conclusion: The results of my doctoral study showed the pathway participants used to learn about SCM practices and how engagement in day-to-day management of DFU occurred. It has also helped to uncover inequities in accessing formal SCM education and specialized services and resources, which in turn contribute to inform nursing practice, education, and health policy.
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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 | 0.037 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".