31 research outputs found
ANALISIS PEMENUHAN INDIKATOR DALAM SISTEM KAPITASI BERBASIS KOMITMEN (KBK) TERHADAP PEMBAYARAN DAN PEMANFAATAN DANA KAPITASI DI PUSKESMAS TUMINTING KOTA MANADO
Perbedaan besaran kapitasi yang diterima oleh setiap FKTP disebabkan oleh perbedaan pada jumlah peserta yang terdaftar maupun hasil kredensialing dan rekredensialing yang dilakukan oleh BPJS Kesehatan. Kapitasi Berbasis Komitmen (KBK) mengharuskan FKTP memenuhi target indikator Angka Kontak (AK) ≥ 150 per mil, Rasio Rujukan Non Spesialistik (RRNS) < 5%, Rasio Peserta Prolanis Berkunjung (RPPB) ≥ 50% dan indikator tambahan khusus untuk Puskesmas yakni Rasio Kunjungan Rumah (RKR) 8,33%. Penelitian ini merupakan jenis penelitian kualitatif menggunakan teknik non-probabilitas dengan prinsip kesesuaian dimana informan penelitian berjumlah 8 orang. Instrumen penelitian berupa pedoman wawancara, alat perekam suara, kamera digital dan alat tulis menulis. Data dikumpulkan melalui proses wawancara mendalam dan telaah dokumen. Hasil penelitian menunjukkan bahwa pemenuhan target indikator dalam sistem KBK memengaruhi besaran pembayaran kapitasi. Pemanfaatan dana kapitasi di Puskesmas Tuminting telah sesuai dengan Permenkes Nomor 21 Tahun 2016, dimana 60% dialokasikan untuk jasa pelayanan dan 40% untuk biaya operasional sedangkan pemanfaatan dana sisa dapat digunakan sesuai dengan porsi pengalokasiannya. Kesimpulan dari penelitian ini adalah indikator AK tidak terpenuhi pada bulan Desember 2018 sebesar 148,62 permil yang disebabkan oleh karena penambahan jumlah peserta PBI dan banyaknya hari libur, indikator RRNS dan RPPB berada pada zona aman selama periode tahun 2018 sedangkan indikator tambahan RKR tidak diperhitungkan dan tidak dimasukkan dalam pelaporan bulanan ke BPJS Kesehatan. Pembayaran kapitasi tidak sesuai dengan Permenkes Nomor 52 Tahun 2016 sedangkan pemanfaatan dana kapitasi telah sesuai dengan Permenkes Nomor 21 Tahun 2016. Kata Kunci: Kapitasi Berbasis Komitmen, Pembayaran Kapitasi, Pemanfaatan KapitasiABSTRACTThe difference in the amount of capitation received by each FKTP is due to the difference in the number of registered participants as well as the results of the credentials and recreditation conducted by BPJS Health. Commitment-based capitation (CBC) requires FKTP to meet the target Contact Number indicator (CN) ≥ 150 per mile, Non-Specialty Referral Ratio (NSRR) <5%, Prolanis Participant Ratio (PPR) ≥ 50% and additional indicators specifically for Health Center namely Ratio Home Visits (RHV) 8.33%. This research type is qualitative research using non-probability techniques with the principle of conformity in which there are 8 research informants. The research instruments in the form of interview guidelines, voice recording devices, digital cameras and writing instruments. The data was collected through a process of in-depth interviews and document review. The result showed that meeting the target indicators in the CBC system affected the capitation payment amount. Utilization of capitation funds at the Tuminting Health Center is in accordance with Minister of Health Regulation Number 21 year 2016, where 60% is allocated for services and 40% for operational costs while utilization of the remaining funds can be used in accordance with the portion of the allocation. The conclusion of this research is the CN indicator was not fulfilled in December 2018 at 148.62 per million due to the addition of PBI participants and the number of holidays, NSRR and PPR indicators were in the safe zone during the 2018 period while the additional RHV indicators were not taken into account and not included in monthly reporting to BPJS Health. The capitation payment is not in accordance with Minster of Health Regulation Number 52 Year 2016 while the utilization of capitation funds has been in accordance with Minster of Health Regulation Number 21 Year 2016. Keywords: Commitment Based Capitation, Capitation Payments, Utilization of Capitatio
Efficient Few-Shot Learning Without Prompts
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and
pattern exploiting training (PET), have achieved impressive results in
label-scarce settings. However, they are difficult to employ since they are
subject to high variability from manually crafted prompts, and typically
require billion-parameter language models to achieve high accuracy. To address
these shortcomings, we propose SetFit (Sentence Transformer Fine-tuning), an
efficient and prompt-free framework for few-shot fine-tuning of Sentence
Transformers (ST). SetFit works by first fine-tuning a pretrained ST on a small
number of text pairs, in a contrastive Siamese manner. The resulting model is
then used to generate rich text embeddings, which are used to train a
classification head. This simple framework requires no prompts or verbalizers,
and achieves high accuracy with orders of magnitude less parameters than
existing techniques. Our experiments show that SetFit obtains comparable
results with PEFT and PET techniques, while being an order of magnitude faster
to train. We also show that SetFit can be applied in multilingual settings by
simply switching the ST body. Our code is available at
https://github.com/huggingface/setfit and our datasets at
https://huggingface.co/setfit
Carbon fluxes resulting from land-use changes in the Tamaulipan thornscrub of northeastern Mexico
Information on carbon stock and flux resulting from land-use changes in subtropical, semi-arid ecosystems are important to understand global carbon flux, yet little data is available. In the Tamaulipan thornscrub forests of northeastern Mexico, biomass components of standing vegetation were estimated from 56 quadrats (200 m2 each). Regional land-use changes and present forest cover, as well as estimates of soil organic carbon from chronosequences, were used to predict carbon stocks and fluxes in this ecosystem