[Analysis of qualitative data collection methods used in adolescent research].
Bibliographic record
Abstract
INTRODUCTION: There has been remarkable growth in research on adolescents in the last decade, particularly in nursing science. OBJECTIVE: The goal of this article is to produce a synthesis of findings justifying the use of qualitative methods in collecting data from adolescents. METHOD: A literature review identified relevant articles (N : 27) from digital databases. FINDINGS: While the studies done on adolescents were on different topics, the data collection methods were often similar. Most of the studies used more than one technique to reconcile scientific rigour and the way the adolescents expressed themselves. In order to understand a phenomenon, its context and the meaning given to the experience proved essential. CONCLUSION: In qualitative research on adolescents, it is important to use data collection methods that make it possible to clearly target the experience explored and to orient and guide the individual in deepening that experience in order to favour the emergence of his or her point of view. Data collection methods based on written communication have to be complemented with other methods more focused on oral communication so as to draw out interpretations reflecting adolescents' points of view as accurately as possible.
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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 | 0.214 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".