Mapping occupational engagement during long-term unemployment: Interconnections and cross-national comparisons of people, places and performances
Notice bibliographique
Résumé
Statement of Purpose: This presentation will report one set of findings from a two-sited, multi-year study of long-term unemployment. Rates of long-term unemployment remain higher than pre-recession estimates despite North American economies’ return to nearly full employment. To understand possibilities and boundaries for occupational engagement within the situation of long-term unemployment, we generated data at three levels in the United States and Canada: we interviewed 15 organizational stakeholders and reviewed organizational documents; we interviewed and observed 18 front-line employment support service providers; and we interviewed, observed, and completed time diaries and/or occupational maps with 23 people who self-identified as being long-term unemployed. In this presentation, we report findings from the occupational mapping process used with 18 participants.\nMethods: Occupational mapping is an elicitation method that is as much about process as it is about product. In our study, we asked participants to hand draw a map to explain the places they regularly traveled within their communities. We prompted participants to describe what was being drawn, the places depicted, activities engaged in within particular places, and modes of travel used. Once the map was completed, we asked participants to reflect on if and how their experience of long-term unemployment had implications for where they went, how they got to places, and the types of activities they needed and wanted to do. We audio-recorded all conversations during the mapping process. Our ongoing analyses of maps and accompanying transcriptions address the types of places and occupations represented; the ways in which maps and transcriptions illuminate social, political, and economic influences on occupation in each study context; common threads between maps; and omissions in maps.\nResults: We will present emerging findings from our occupational mapping process in relation to national context, gender, financial and transportation resources, and family situation. We will also integrate these findings with understandings gained through other analytic approaches used in the study, such as situational analysis and critical narrative inquiry.\nImplications: Occupational mapping can elicit details about everyday doing that are difficult to articulate using narrative methods given the tacit and experiential nature of daily occupations. It can be a useful strategy for understanding interconnections between people, places, and performances of everyday occupations in line with calls to transcend individual perspectives in occupational science. Our findings suggest that this method is a valuable means of illuminating the transactional person-environment relationships that shape occupational engagement during contemporary long-term unemployment.\nDiscussion questions: In what ways can occupational mapping augment other data generation and analysis approaches? How does occupational mapping fit within larger efforts to transcend individual perspectives in occupational science? Within a multi-level, cross-national study of long-term unemployment, what kinds of understandings does occupational mapping yield? \nKey words: Occupational mapping, long-term unemployment, critical qualitative research
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».