The Influence of Electrospinning Parameters and Drug Loading on Polyhydroxyalkanoate (PHA) Nanofibers for Drug Delivery
Bibliographic record
Abstract
Social relationships between staff and youth are often regarded as one of the most important mediums to achieving positive youth development (PYD) program aims (McDavid & McDonough, 2019). Despite the promising impacts of staff and youth relationships within sport-based PYD settings, little research has explored how staff foster connections with youth in these settings—particularly youth who are considered at-risk. The purpose of this interpretative phenomenological analysis was to describe and interpret staff members’ perceptions of how they foster social connections with youth at-risk within after-school, sport-based PYD programs. Purposefully selected participants included ten staff members (eight women and two men), that work within after-school, sport-based PYD programs. Data were generated via one-on-one semi-structured interviews that were conducted either in-person or via Zoom. All interviews were audio-recorded and then transcribed verbatim. Smith and Nizza’s (2022) four-step process of data analysis was used to identify six themes that represent the findings of this study: (1) getting to know, investing, and showing intention towards youth, (2) fostering positive spaces and relationships, (3) allowing for autonomy and leadership, (4) practicing what you preach, (5) awareness of power dynamics, and (6) approaching with caution. Grounded in the experiences of staff members, findings from this study highlight critical considerations and strategies for fostering social connections with youth that may function to improve the experiences of both youth at-risk and staff members in sport-focused PYD programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".