REDUCING SMOKING AMONG INDIGENOUS NURSING STUDENTS USING INCENTIVES
Bibliographic record
Abstract
Māori nurses are seen by the nursing profession and Māori communities as key agents of change for Māori health yet they make up only a small proportion of the nursing workforce. Existing programmes to attract Māori to nursing and to support students include developing their professional identity and strengthening cultural identity. Addressing personal health issues such as smoking, is fundamental to both aspects of identity. Schools of nursing provide the ideal setting to reduce the disproportionately high rates of smoking among Māori nurses. This paper presents the results of a stop smoking trial using a financial incentive to assist Māori nursing students and a whānau (family) quit mate to quit smoking. A marae (traditional meeting place) based 24 week programme of cessation support was offered and financial incentives in the form of scholarship payments were awarded to students incrementally based on proven smoking cessation of both quit mates. Focus groups were held at 2 points in the programme with students and their quit mates and a simple questionnaire was completed by students at the end of the programme. Trying to quit smoking with a whānau member was both an enabler and barrier to cessation. Financial incentives appeared to have an effect on smoking cessation in the last 4 weeks of the programme when the larger portion of the scholarship payment was awarded. # *Te reo Māori translation* # Te whakaheke i te kai paipa i waenga i ngā tapuhi tangata whenua mā te whakamahi whakawhiwhinga **Ngā ariā matua**\ E whakaarotia ana ngā tapuhi Māori e te tini o te kāhui ngāio tapuhi me ngā hapori Māori ētahi o ngā tino kaikawe i ngā tikanga whakapiki i te hauora Māori, engari 7.5% noa iho o ngā kaimahi tapuhi he Māori. Tētahi o ngā kōkiri kukume mai i te iwi Māori ki ngā mahi tapuhi ko ngā kaupapa whakapakari i tō rātou tuakiri ngaio, me tō rātou tū hei Māori. Mō aua rerenga e rua o te tuakiri o te tangata, ko te whakatika i ngā tino take hauora whaiaro o ngā tapuhi nei, pēnei i te kaipaipa tētahi take nui. Ko ngā kura tapuhi tētahi wāhi tino pai pea hei whakaheke i te tokomaha, e ai ki ngā ōrautanga, o ngā tapuhi Māori e kaipaipa ana. Tā tēnei pukapuka he tāpae i ngā hua o tētahi whakamātautau aukati i te kaipaipa mā te whakamahi tikanga whakawhiwhi moni, hei āwhina i ngā ākonga tapuhi Māori me tētahi hoa nō te whānau kia whakarērea te kaipaipa. I haere tētahi kaupapa 24-wiki te roa i te marae me ōna āwhina, tautoko kia whakamutu, me ētahi whakawhiwhinga ā-moni mō ō rātou akoranga ki ngā ākonga, i runga anō i te kaha o ngā hoa tokorua, i āta tirohia, ki te whakamutu rawa. E 2 ngā huihuinga o ētahi rōpū aro whāiti o ngā ākonga me ō rātou hoa whakamutu i tū, ā, ka whakakīa tētahi puka ngāwari e ngā ākonga i te mutunga o te kaupapa. I kitea ko tēnei mahi te whakamutu i te taha o tētahi hoa nō te whānau, ka noho i ētahi wā hei āwhina ki te whakamutu, i ētahi wā hei katinga, maioro rānei. Te āhua nei i kaha ake te pānga o tētahi whakawhiwhinga moni ki te whakarerenga i te kaipapa i ngā wiki e 4 whakamutunga, arā, i te tatanga atu ki te utunga atu o nuinga o te moni.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".