Lost together: Experiences of family physicians with emerging adult mental health.
Bibliographic record
Abstract
OBJECTIVE: To explore the perceptions and experiences of FPs with emerging adult (EA) mental health to inform opportunities for improvement in EA mental health care. DESIGN: Constructivist grounded theory methodology, including theoretical sampling and constant comparative analysis of data to synthesize results. SETTING: Southwestern Ontario. PARTICIPANTS: Twenty practising FPs. METHODS: In-depth, semistructured, in-person interviews, which were audiorecorded and transcribed verbatim. MAIN FINDINGS: Family physicians recognized the unique situation of EAs being between adolescence and adulthood, having heavy psychosocial needs, and lacking a connection to the health care system. Experience and confidence are needed to treat the EA population, but provision of mental health care to EAs is influenced by resources, knowledge, and communication. Family physicians noted that they are the default physician while EAs wait for specialized care, and are often the physicians that the patient is referred back to after specialized care. Often, the FP knows and treats the EA's entire family, which participants described as enabling them to understand the EA's unique context. CONCLUSION: Family physicians and EAs are "lost together" in a fragmented health care system. Family physicians have the unique potential to assist EAs with their mental health needs, but that is not being actualized. Family physicians can support mental health outcomes for EAs through an improvement in knowledge and skills, and through forming family practice teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".