Pathways and identity: toward qualitative research careers in child and adolescent psychiatry
Bibliographic record
Abstract
Abstract Objective Qualitative research methods are based on the analysis of words rather than numbers; they encourage self-reflection on the investigator’s part; they are attuned to social interaction and nuance; and they incorporate their subjects’ thoughts and feelings as primary sources. Despite appearing well suited for research in child and adolescent psychiatry (CAP), qualitative methods have had relatively minor uptake in the discipline. We conducted a qualitative study of CAPs involved in qualitative research to learn about these investigators’ lived experiences, and to identify modifiable factors to promote qualitative methods within the field of youth mental health. Methods We conducted individual, semi-structured 1-h long interviews through Zoom. Using purposive sample, we selected 23 participants drawn from the US (n = 12) and from France (n = 11), and equally divided in each country across seniority level. All participants were current or aspiring CAPs and had published at least one peer-reviewed qualitative article. Ten participants were women (44%). We recorded all interviews digitally and transcribed them for analysis. We coded the transcripts according to the principles of thematic analysis and approached data analysis, interpretation, and conceptualization informed by an interpersonal phenomenological analysis (IPA) framework. Results Through iterative thematic analysis we developed a conceptual model consisting of three domains: (1) Becoming a qualitativist: embracing a different way of knowing (in turn divided into the three themes of priming factors/personal fit; discovering qualitative research; and transitioning in); (2) Being a qualitativist: immersing oneself in a different kind of research (in turn divided into quality: doing qualitative research well; and community: mentors, mentees, and teams); and (3) Nurturing: toward a higher quality future in CAP (in turn divided into current state of qualitative methods in CAP; and advocating for qualitative methods in CAP). For each domain, we go on to propose specific strategies to enhance entry into qualitative careers and research in CAP: (1) Becoming: personalizing the investigator’s research focus; balancing inward and outward views; and leveraging practical advantages; (2) Being: seeking epistemological flexibility; moving beyond bibliometrics; and the potential and risks of mixing methods; and (3) Nurturing: invigorating a quality pipeline; and building communities. Conclusions We have identified factors that can support or impede entry into qualitative research among CAPs. Based on these modifiable findings, we propose possible solutions to enhance entry into qualitative methods in CAP (pathways), and to foster longer-term commitment to this type of research (identity).
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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 | 0.268 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.020 | 0.052 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".