Healthcare providers’ perspectives and experiences caring for individuals with lower limb absence during the perinatal period: a qualitative study
Bibliographic record
Abstract
PURPOSE: This study explored healthcare providers' (HCPs') experiences providing care for individuals with lower limb amputation (LLA) during the perinatal period, the perceived impact of pregnancy on individuals with LLA, and strategies to improve perinatal support for individuals with LLA. METHODS: This is a qualitative descriptive study. Semi-structured interviews were conducted with HCPs with experience caring for individuals with LLA during the perinatal period. A thematic analysis was conducted, informed by the DEPICT model. RESULTS: = 5) participated. Three themes were identified that describe HCPs' perspectives on the impact of pregnancy on LLA and their approach to providing care during the perinatal period: (1) Clinical Approach: Problem Solving and Preparation, (2) Managing the Physical Impacts of Pregnancy by Maintaining Mobility, and (3) Tailoring Care to Individual Needs. Participants described using clinical expertise and trial and error to support individuals during pregnancy with limited education and resources. They reported variability in patients' physical (e.g. swelling, fatigue) and psychosocial needs necessitating tailored management. CONCLUSION: There is a lack of resources and education for clinicians regarding pregnancy and LLA. While clinicians managed this by using clinical reasoning and planning ahead, more resources for HCPs who provide care are needed.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".